Systems and methods for modeling agricultural environments

Abstract

In some aspects, the present disclosure provides a computer-implemented method for generating synthetic data to train artificial intelligence (Al) models. The computer-implemented method can comprise receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects. In some aspects, the present disclosure provides a computer-implemented method comprising processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories. In some aspects, the present disclosure provides a system comprising a digital twin of an agricultural environment comprising a plurality of virtual agents that tracks a plurality of real entities at an entity-level resolution.

Description

WSGR Docket No.: 69213-701.601SYSTEMS AND METHODS FOR MODELING AGRICULTURAL ENVIRONMENTSCROSS-REFERENCE

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 728,004, filed December 4, 2024, which is incorporated by reference herein in its entirety.BACKGROUND

[0002] The world population is expected to reach about 10 billion by 2050. To feed the world’s population in 2050, today’s agricultural industry productivity should increase by about 70%. However, projections based on current trends predict that productivity will increase only by 30%. Technical challenges in scaling the productivity include overcoming labor shortages, adapting to climate change, and controlling for epidemics among crops and livestock.

[0003] In particular, the agricultural industry in the US is experiencing major labor shortages and sudden disease risks, putting the country’s and the world’s food and health security at risk. For example, US agricultural productivity has tripled since 1950 and labor has decreased by over 75%. Productivity demands will continue to rise and available labor will continue to decline. Also, the workforce has seen various stressors, like the COVID- 19 pandemic, that limited productivity or forced workers into unsafe conditions. In recent years, and with a limited pool of available workers, these stresses bring additional food security concerns. For example, health concerns from livestock farming have also become an increased food security concern. The recent bird flu pandemic has been responsible for 100s of millions of birds lost. Recently, this virus has been found to spread from poultry to humans, raising alarms on transmission threats. Disease spread is an issue in a country that raises nearly 10 billion chickens a year.

[0004] Therefore, there is a need for systems and methods that can at least improve food security and workforce efficiencies thereby keeping productivity in pace with demand, addressing unpredictable workforce stressors; and detecting disease precursors in a timely manner.SUMMARY

[0005] Systems and methods herein can provide technical solutions for solving at least technical challenges in scaling productivity. For example, the present application provides an Al platform for improving production environments by revealing untapped resources for productivity and efficiency- 1 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601gains. The Al platform can allow users (e.g., farmers) to achieve greater operational, environmental, and social sustainability from their production environments (e.g., farms). The Al platform can involve replicating a physical environment with a virtual environment, training models based on synthetic data generated using the virtual environment, and applying models to make predictions on the physical environment.

[0006] In some aspects, the present disclosure provides a computer-implemented method, comprising: (a) receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects; (b) processing the synthetic data to train a machine learning (ML) model to yield a trained ML model; (c) using the trained ML model to predict a plurality of attributes of a physical environment; and (d) determining, based at least on the predicting in (c), one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent. In some embodiments, the computer-implemented method further comprises generating the synthetic data, wherein the synthetic data comprises a log of a plurality of attributes of each virtual sensor, virtual agent, or virtual object. In some embodiments, the computer-implemented method further comprises using the log to automatically train the trained ML model, wherein the automatic training comprises supervised training or unsupervised training. In some embodiments, the physical environment comprises a plurality of (i) real sensors, (ii) real agents, or (iii) real objects. In some embodiments, the plurality of attributes comprises attributes associated with (i) each virtual sensor or real sensor, (ii) each virtual agent or real agent, or (iii) each virtual object or real object. In some embodiments, the trained ML model comprises a sensor-attribute model configured to (i) analyze data from the plurality of virtual sensors or real sensors and (ii) determine at least one attribute of the plurality of attributes. In some embodiments, generating the sensor-attribute model comprises: (a) receiving sensor data from (i) the plurality of virtual sensors, (ii) the plurality of real sensors, or both (i) and (ii); (b) processing the sensor data to determine attributes of the plurality of virtual agents or real agents; and (c) updating the synthetic data from the processing in (b) for use by the trained ML model based at least on the determined attributes. In some embodiments, the computer-implemented method further comprises updating the sensor-attribute model based at least on training the sensor-attribute model with the updated synthetic data. In some embodiments, the computer-implemented method further comprises generating the synthetic data, wherein the synthetic data comprises a log of a plurality of attribute data in the virtual environment. In some embodiments, the computer-implemented method further comprises using the log to automatically train the sensor-- 2 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601attribute model, wherein the automatic training comprises supervised training or unsupervised training. In some embodiments, the trained ML model further comprises an attribute-entity model configured to uniquely associate attributes to (i) each virtual agent, (ii) each real agent, or both (i) and (ii). In some embodiments, generating the attribute-entity model comprises: (a) receiving determined attributes from the sensor-attribute model and applying the determined attributes to (i) the virtual sensors, (ii) the real sensors, or both (i) and (ii); (b) processing the determined attributes to determine unique attributes for (i) each virtual agent, (ii) each real agent, or both (i) and (ii); and (c) updating the synthetic data from the processing in (b) for use by the trained ML model based at least on the determined unique attributes. In some embodiments, the computer-implemented method further comprises updating the attribute- entity model based at least on training the attribute- entity model with the updated synthetic data. In some embodiments, the computer-implemented method further comprises generating the synthetic data, wherein the synthetic data comprises a log of a plurality of entity data and attribute data in the virtual environment. In some embodiments, the computer-implemented method further comprises using the log to automatically train the attribute- entity model, wherein the automatic training comprises supervised training or unsupervised training. In some embodiments, the trained ML model further comprises an entity -attribute projection model configured to predict the plurality of attributes at one or more future time periods with a predetermined confidence level. In some embodiments, generating the entity-attribute projection model comprises: (a) receiving time-history data from (i) the virtual environment, (ii) the physical environment, or both (i) and (ii); (b) processing the time-history data to predict an attribute over time for (i) each virtual agent, (ii) each real agent, or both (i) and (ii); and (c) updating the synthetic data from the processing in (b) for use by the trained ML model based at least on actual attributes or the predicted attributes. In some embodiments, the computer-implemented method further comprises updating the entity-attribute projection model based at least on training the entity-attribute projection model with the updated synthetic data. In some embodiments, the computer-implemented method further comprises generating the synthetic data, wherein the synthetic data comprises a log of the time-history data in the virtual environment. In some embodiments, the computer-implemented method further comprises using the log to automatically train the entity-attribute projection model, wherein the automatic training comprises supervised training or unsupervised training. In some embodiments, the trained ML model comprises each of (i) the sensor-attribute model, (ii) the attribute-entity model, and (iii) the entity-attribute projection model. In some embodiments, the computer-implemented method further- 3 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601comprises iteratively updating the trained ML model based at least on processing data from the plurality of virtual sensors or real sensors. In some embodiments, the computer-implemented method further comprises using a stochastic model to generate attributes for each virtual agent, wherein the attributes comprise visual attributes, behavior attributes, or health attributes. In some embodiments, the virtual environment or the physical environment comprises an agricultural development facility, a production facility, or a processing facility for use with livestock, animals, aquaculture, row crops, greenhouse produce, greenhouse horticulture, or other agricultural sectors. In some embodiments, the plurality of attributes of the virtual environment or the physical environment comprises (i) environmental attributes of weather, temperature, humidity levels, precipitation, airflow, natural lighting, artificial lighting, soil characteristics, disease pressure, or bedding type, (ii) global attributes of agent density, agent uniformity, or a number of agents, or (iii) interactive attributes of feed type, feed delivery, feed availability, water delivery, water availability, or types of management practices, wherein the types of management practices comprise adjusting one or more parameters of chemical solutions, nutrient solutions, or physical solutions to alter at least one attribute of a real agent. In some embodiments, the plurality of attributes of each virtual sensor or real sensor comprises attributes associated with imaging detectors, radio frequency identification (RFID) detectors, motion detectors, pressure detectors, sound detectors, temperature detectors, humidity detectors, water detectors, chemical detectors, ammonia detectors, carbon dioxide detectors, hydrogen sulfide detectors, nitrogen detectors, or light detectors. In some embodiments, the plurality of virtual agents or real agents comprises animals, plants, fungi, protists, or bacteria. In some embodiments, the plurality of attributes of each virtual agent or real agent comprises attributes associated with weight, size, height, color, appearance, visual properties, activity level, feed conversion ratio, nutrient content, growth rate, growth stage, mortality risk, propensity for disease, health metric, propensity for a type of behavior, stress, or social behavior. In some embodiments, the plurality of attributes for each virtual object or real object comprises attributes associated with shelter structures, shelter enclosures, shelter subenclosures, bedding, soil, physical barriers, sensor calibration objects, waterers, feeders, irrigation systems, fans, feeders, light sources, sprayers, tractors, agricultural equipment, or physical implements. In some embodiments, the computer-implemented method further comprises using the method to create a management plan based at least on the one or more actions. In some embodiments, the computer-implemented method further comprises using the method for genetic selection of a virtual agent or a real agent determined to exhibit improved attributes over a baseline virtual agent or- 4 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601real agent. In some embodiments, the computer-implemented method further comprises using the method to monitor health attributes in real time for each real agent. In some embodiments, the computer-implemented method further comprises using the method to design, optimize, or improve a design of the physical environment over a baseline physical environment. In some embodiments, the one or more actions comprise adjusting (i) environmental parameters of the physical environment, (ii) feeding, watering, or other management parameters for the plurality of real agents, or (iii) density of the plurality of real agents. In some embodiments, the computer-implemented method further comprises using a ML model to generate the virtual environment and the plurality of attributes of the virtual sensors, agents, and objects from data of the real sensors, agents, and objects received from the plurality of real sensors. In some embodiments, the computer-implemented method further comprises using actual data from the physical environment to iteratively update the trained ML model. In some embodiments, the computer-implemented method further comprises prior to (a), developing the virtual environment to generate synthetic data, wherein an initial state of each virtual agent is determined by receiving observations of the plurality of real agents. In some embodiments, the computer-implemented method further comprises displaying the one or more actions on a user interface (UI) configured with operable user tools which allow a user to select at least one action of the one or more of actions. In some embodiments, the synthetic data is associated with attributes of the plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects. In some aspects, the present disclosure provides a system comprising at least one processor and instructions executable by the at least one processor to cause the at least one processor to perform operations comprising: (a) receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects; (b) processing the synthetic data to train a machine learning (ML) model to yield a trained ML model; (c) using the trained ML model to predict a plurality of attributes of a physical environment and a plurality of attributes of real agents in the physical environment; and (d) determining, based at least on the predicting in (c), one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent. In some embodiments, the operations comprise generating the synthetic data, wherein the synthetic data comprises a log of a plurality of attributes of each virtual sensor, virtual agent, or virtual object. In some embodiments, the operations comprise using the log to automatically train the trained ML model, wherein the automatic training comprises supervised training or unsupervised training. In some embodiments, the physical environment comprises a plurality of (i) real sensors, (ii) real agents, or- 5 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601(iii) real objects. In some embodiments, the plurality of attributes comprises attributes associated with (i) each virtual sensor or real sensor, (ii) each virtual agent or real agent, or (iii) each virtual object or real object. In some embodiments, the trained ML model comprises a sensor-attribute model configured to (i) analyze data from the plurality of virtual sensors or real sensors and (ii) determine at least one attribute of the plurality of attributes. In some embodiments, generating the sensor-attribute model comprises receiving sensor data from (i) the plurality of virtual sensors, (ii) the plurality of real sensors, or both (i) and (ii). In some embodiments, generating the sensor-attribute model comprises processing the sensor data to determine attributes of the plurality of virtual agents or real agents. In some embodiments, generating the sensor-attribute model comprises updating the synthetic data for use by the trained ML model based at least on the determined attributes. In some embodiments, the operations comprise updating the sensor-attribute model based at least on training the sensor-attribute model with the updated synthetic data. In some embodiments, the operations comprise generating the synthetic data, wherein the synthetic data comprises a log of a plurality of attribute data in the virtual environment. In some embodiments, the operations comprise using the log to automatically train the sensor-attribute model, wherein the automatic training comprises supervised training or unsupervised training. In some embodiments, the trained ML model further comprises an attribute-entity model configured to uniquely associate attributes to (i) each virtual agent, (ii) each real agent, or both (i) and (ii). In some embodiments, generating the attribute-entity model comprises receiving determined attributes from the sensor-attribute model. In some embodiments, generating the attribute-entity model comprises applying the determined attributes to (i) the virtual sensors, (ii) the real sensors, or both (i) and (ii). In some embodiments, generating the attribute-entity model comprises processing the determined attributes to determine unique attributes for (i) each virtual agent, (ii) each real agent, or both (i) and (ii). In some embodiments, generating the attribute-entity model comprises updating the synthetic data for use by the trained ML model based at least on the determined unique attributes. In some embodiments, the operations comprise updating the attribute-entity model based at least on training the attribute-entity model with the updated synthetic data. In some embodiments, the operations comprise generating the synthetic data, wherein the synthetic data comprises a log of a plurality of entity data and attribute data in the virtual environment. In some embodiments, the operations comprise using the log to automatically train the attribute-entity model, wherein the automatic training comprises supervised training or unsupervised training. In some embodiments, the trained ML model further comprises an entity-attribute projection model configured to predict the- 6 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601plurality of attributes at one or more future time periods with a predetermined confidence level. In some embodiments, generating the entity-attribute projection model comprises receiving time-history data from (i) the virtual environment, (ii) the physical environment, or both (i) and (ii). In some embodiments, generating the entity -attribute projection model comprises processing the time-history data to predict an attribute over time for (i) each virtual agent, (ii) each real agent, or both (i) and (ii). In some embodiments, generating the entity-attribute projection model comprises updating the synthetic data for use by the trained ML model based at least on actual attributes or the predicted attributes. In some embodiments, the operations comprise updating the entity-attribute projection model based at least on training the entity-attribute projection model with the updated synthetic data. In some embodiments, the operations comprise generating the synthetic data. In some embodiments, the synthetic data comprises a log of the time-history data in the virtual environment. In some embodiments, the operations comprise using the log to automatically train the entity-attribute projection model. In some embodiments, the automatic training comprises supervised training or unsupervised training. In some embodiments, the trained ML model comprises each of (i) the sensor-attribute model, (ii) the attribute-entity model, and (iii) the entity-attribute projection model. In some embodiments, the operations comprise iteratively updating the trained ML model based at least on processing data from the plurality of virtual sensors or real sensors. In some embodiments, the operations comprise using a stochastic model to generate attributes for each virtual agent. In some embodiments, the attributes comprise visual attributes, behavior attributes, or health attributes. In some embodiments, the attributes comprise activity, physical abnormality (e.g., limping), disposition to a disease or sudden death syndrome, or any combination thereof. In some embodiments, the virtual environment or the physical environment comprises an agricultural development facility, a production facility, or a processing facility for use with livestock, animals, aquaculture, row crops, greenhouse produce, greenhouse horticulture, or other agricultural sectors. In some embodiments, the plurality of attributes of the virtual environment or the physical environment comprises (i) environmental attributes of weather, temperature, humidity levels, precipitation, airflow, natural lighting, artificial lighting, soil characteristics, disease pressure, or bedding type, (ii) global attributes of agent density, agent uniformity, or a number of agents, or (iii) interactive attributes of feed type, feed delivery, feed availability, water delivery, water availability, or types of management practices, wherein the types of management practices comprise adjusting one or more parameters of chemical solutions, nutrient solutions, or physical solutions to alter at least one attribute of a real agent. In some embodiments, the- 7 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601plurality of attributes of each virtual sensor or real sensor comprises attributes associated with imaging detectors, radio frequency identification (RFID) detectors, motion detectors, pressure detectors, sound detectors, temperature detectors, humidity detectors, water detectors, chemical detectors, ammonia detectors, carbon dioxide detectors, hydrogen sulfide detectors, nitrogen detectors, or light detectors. In some embodiments, the plurality of virtual agents or real agents comprises animals, plants, fungi, protists, or bacteria. In some embodiments, the plurality of attributes of each virtual agent or real agent comprises attributes associated with weight, size, height, color, appearance, visual properties, activity level, feed conversion ratio, nutrient content, growth rate, growth stage, mortality risk, propensity for disease, health metric, propensity for a type of behavior, stress, or social behavior. In some embodiments, the plurality of attributes for each virtual object or real object comprises attributes associated with shelter structures, shelter enclosures, shelter sub-enclosures, bedding, soil, physical barriers, sensor calibration objects, waterers, feeders, irrigation systems, fans, feeders, light sources, sprayers, tractors, agricultural equipment, or physical implements. In some embodiments, the operations comprise creating a management plan based at least on the one or more actions. In some embodiments, the operations comprise performing genetic selection of a virtual agent or a real agent determined to exhibit improved attributes over a baseline virtual agent or real agent. In some embodiments, the operations comprise monitoring health attributes in real time for each real agent. In some embodiments, the operations comprise designing, optimizing, or improving a design of the physical environment over a baseline physical environment. In some embodiments, the one or more actions comprise adjusting (i) environmental parameters of the physical environment, (ii) feeding, watering, or other management parameters for the plurality of real agents, or (iii) density of the plurality of real agents. In some embodiments, the operations comprise using a ML model to generate the virtual environment and the plurality of attributes of the virtual sensors, agents, and / or objects from data of the real sensors, agents, and / or objects received from the plurality of real sensors. In some embodiments, the operations comprise using actual data from the physical environment to iteratively update the trained ML model. In some embodiments, the operations comprise developing the virtual environment to generate synthetic data. In some embodiments, an initial state of each virtual agent is determined by receiving observations of the plurality of real agents. In some embodiments, the operations comprise displaying the one or more actions on a user interface (UI) configured with operable user tools which allow a user to select at least one action of the one or more of actions. In some embodiments, the synthetic data is associated with attributes of the plurality of (i) virtual sensors,- 8 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601(ii) virtual agents, or (iii) virtual objects. In some embodiments, the operations comprise providing an alert to a user. In some embodiments, the alert can be provided in real-time as events occur in the physical environment. In some embodiments, the alert can provide a notification of a dead or dying animal, unusual behavior of an animal, onset of a disease, or any combination thereof. In some embodiments, the alert can provide a notification of a dead or dying bird, unusual behavior of a bird, unusual behavior of a flock of birds, onset of a disease in a bird, or any combination thereof. In some embodiments, the alert can recommend an action to take, wherein taking the action can improve the operations of the facility. For example, an action can be adjusting the environmental conditions to increase feed uptake or change feed nutrition, or adjusting any other operations to improve performance of the entities.

[0007] In some aspects, the present disclosure provides a computer-implemented method comprising processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories. In some embodiments, the processing comprises tracking each of the one or more identifiers for each of the one or more organisms. In some embodiments, the method further comprises obtaining one or more biological or behavioral features throughout the one or more trajectories. In some embodiments, the method further comprises assigning the one or more biological or behavioral features to the one or more organisms. In some embodiments, the method further comprises assigning one or more phenotypes to the one or more organisms based on the one or more biological or behavioral features. In some embodiments, a subset of the one or more phenotypes are not available for a subset of the one or more organisms. In some embodiments, the method further comprises generating estimates of the subset of the one or more phenotypes for the subset of the one or more organisms. In some embodiments, the method further comprises combining one or more features along the one or more trajectories to build one or more models for estimating one or more unavailable features along that trajectory, wherein the one or more features comprise the one or more identifiers, the one or more biological or behavioral features, the one or more phenotypes, or any combination thereof. In some embodiments, the one or more identifiers comprise one identifier. In some embodiments, the one or more biological or behavioral features comprise one biological or behavioral feature. In some embodiments, the one or more phenotypes comprise one phenotype. In some embodiments, the one or more trajectories comprise a plurality of trajectories. In some embodiments, the one or more organisms comprise a plurality of organisms. In some embodiments, the one or more identifiers comprise a plurality of identifiers. In some embodiments, the one or more- 9 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601biological or behavioral features comprise a plurality of biological or behavioral features. In some embodiments, the one or more phenotypes comprise a plurality of phenotypes. In some embodiments, each of the one or more trajectories comprise a discrete trajectory of a discrete organism of the one or more organisms. In some embodiments, the one or more organisms comprise one or more livestock animals or one or more plants. In some embodiments, the one or more livestock animals comprise one or more birds, swine, cattle, goats, sheep, or any combination thereof. In some embodiments, the method further comprises obtaining the one or more trajectories. In some embodiments, the obtaining the one or more trajectories comprises receiving, generating, or measuring the one or more trajectories. In some embodiments, the measuring is performed using vision tracking, visual identifiers, ultra-high or high frequency RFID, lidar, or any combination thereof. In some embodiments, the measuring is performed using a plurality of cameras. In some embodiments, the measuring is performed using image segmentation based on the plurality of cameras. In some embodiments, the one or more trajectories comprise one or more 2D or 3D trajectories. In some embodiments, the one or more trajectories are measured from an environment comprising one or more obstructions or obstacles. In some embodiments, the one or more obstructions or obstacles comprise: a food source, a water source, a shelter, or any combination thereof. In some embodiments, the obtaining the one or more biological or behavioral features comprises receiving, generating, or measuring the one or more biological or behavioral features. In some embodiments, the measuring is performed using a sensor, a human intervention, a model, or any combination thereof. In some embodiments, the model is a vision to weight model. In some embodiments, the one or more biological or behavioral features comprise: a feeding event, a drinking event, a urinating event, a defecating event, a mating event, a reproducing event, a production event, an injury event, an illness event, a death event, an aggression event, a socializing event, a weight, a height, a movement pattern, a visual, a diagnosis, a prognosis, a symptom, an epidermal feature, or any combination thereof. In some embodiments, the epidermal feature comprises a visual appearance or optical feature of feather, fur, hair, beak, eyes, skin, or any combination thereof. In some embodiments, the method further comprises obtaining the one or more identifiers. In some embodiments, the obtaining the one or more identifiers comprises receiving, generating, or measuring the one or more identifiers. In some embodiments, the measuring is performed by detecting a visual identifier, a tag, a barcode, RFID, human annotation, or any combination thereof. In some embodiments, the assigning one or more phenotypes is performed using a model. In some embodiments, the model is a deterministic model the processes (i) annotated- 10 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601phenotype data of a first subset of the one or more organisms and (ii) sensor data of a second subset of the one or more organisms, to determine the one or more phenotypes for the second subset of the one or more organisms. In some embodiments, the model is a machine learning model trained to process one or more images to determine the one or more phenotypes. In some embodiments, the one or more phenotypes comprise: a health state, an injury state, a disease state, a feather score, a health score, a death state, a disease score, an activity score, feeding time, drinking time, number of eggs laid, time spent in a nest box, total distance traveled, weight, time in scratch area, time on slats, confidence score of any of the preceding, or any combination thereof. In some embodiments, the method further comprises isolating an organism from the one or more organisms based on a phenotype of the organism. In some embodiments, the phenotype of the organism comprises an infectious disease state, lameness state, or any other unhealthy, non-productive, or risk inducing state.

[0008] In some aspects, the present disclosure provides a computer-implemented method of training a neural network, comprising: collecting (i) one or more trajectories of one or more organisms and (ii) one or more biological or behavioral features of one or more organisms throughout the one or more trajectories; processing the (i) one or more trajectories of one or more organisms and (ii) the one or more biological or behavioral features of one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms; and training the machine learning model to reduce a loss function that quantifies an error associated with the one or more predictions of the one or more phenotypes compared to one or more ground- truth values of the one or more phenotypes. In some embodiments, the one or more trajectories, the one or more biological or behavioral features, the one or more ground-truth values, or any combination thereof, comprises real data, synthetic data, or both.

[0009] In some aspects, the present disclosure provides a computer- implemented method of predicting one or more phenotypes of one or more organisms, comprising: processing (i) one or more trajectories of the one or more organisms and (ii) one or more biological or behavioral features of the one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms. In some embodiments, the method further comprises processing (iii) a second set of one or more phenotypes of a second set of one or more organisms to generate the one or more predictions of the one or more phenotypes of the one or more organisms.

[0010] In some aspects, the present disclosure provides a system comprising one or more processors independently or collectively configured for processing one or more trajectories of one or more- 11 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories. In some embodiments, the processing comprises tracking each of the one or more identifiers for each of the one or more organisms. In some embodiments, the system is further configured for obtaining one or more biological or behavioral features throughout the one or more trajectories. In some embodiments, the system is further configured for assigning the one or more biological or behavioral features to the one or more organisms. In some embodiments, the system is further configured for assigning one or more phenotypes to the one or more organisms based on the one or more biological or behavioral features. In some embodiments, a subset of the one or more phenotypes are not available for a subset of the one or more organisms. In some embodiments, the system is further configured for generating estimates of the subset of the one or more phenotypes for the subset of the one or more organisms. In some embodiments, the system is further configured for combining one or more features along the one or more trajectories to build one or more models for estimating one or more unavailable features along that trajectory, wherein the one or more features comprise the one or more identifiers, the one or more biological or behavioral features, the one or more phenotypes, or any combination thereof. In some embodiments, the one or more organisms comprise one or more livestock animals. In some embodiments, the one or more livestock animals comprise one or more birds. In some embodiments, the system is further configured for obtaining the one or more trajectories. In some embodiments, the obtaining the one or more trajectories comprises receiving, generating, or measuring the one or more trajectories. In some embodiments, the measuring is performed using vision tracking, visual identifiers, ultra-high or high frequency RFID, a lidar, or any combination thereof. In some embodiments, the measuring is performed using a plurality of cameras. In some embodiments, the measuring is performed using image segmentation based on the plurality of cameras. In some embodiments, the one or more trajectories comprise one or more 2D or 3D trajectories. In some embodiments, the one or more trajectories are measured from an environment comprising one or more obstructions or obstacles. In some embodiments, the one or more obstructions or obstacles comprise: a food source, a water source, a shelter, or any combination thereof. In some embodiments, the obtaining the one or more biological or behavioral features comprises receiving, generating, or measuring the one or more biological or behavioral features. In some embodiments, the measuring is performed using a sensor, a human intervention, a model, or any combination thereof. In some embodiments, the model is a vision to weight model. In some embodiments, the one or more biological or behavioral features comprise: a feeding event, a drinking event, a urinating event, a- 12 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601defecating event, a mating event, a reproducing event, a production event, an injury event, an illness event, a death event, an aggression event, a socializing event, a weight, a height, a movement pattern, a visual, a diagnosis, a prognosis, a symptom, an epidermal feature, or any combination thereof. In some embodiments, the epidermal feature comprises a visual appearance or optical feature of feather, fur, hair, beak, eyes, skin, or any combination thereof. In some embodiments, the system is further configured for obtaining the one or more identifiers. In some embodiments, the obtaining the one or more identifiers comprises receiving, generating, or measuring the one or more identifiers. In some embodiments, the measuring is performed by detecting a visual identifier, a tag, a barcode, RFID, human annotation, or any combination thereof. In some embodiments, the assigning one or more phenotypes is performed using a model. In some embodiments, the model is a deterministic model the processes (i) annotated phenotype data of a first subset of the one or more organisms and (ii) sensor data of a second subset of the one or more organisms, to determine the one or more phenotypes for the second subset of the one or more organisms. In some embodiments, the model is a machine learning model trained to process one or more images to determine the one or more phenotypes. In some embodiments, the one or more phenotypes comprise: a health state, an injury state, a disease state, a feather score, a health score, a death state, a disease score, an activity score, feeding time, drinking time, number of eggs laid, time spent in a nest box, total distance traveled, weight, time in scratch area, time on slats, confidence score of any of the preceding, or any combination thereof. In some embodiments, the system is further configured for isolating an organism from the one or more organisms based on a phenotype of the organism. In some embodiments, the phenotype of the organism comprises an infectious disease state, lameness state, or any other unhealthy, nonproductive, or risk inducing state.

[0011] In some aspects, the present disclosure provides a system comprising one or more processors independently or collectively configured for training a machine learning model by: collecting (i) one or more trajectories of one or more organisms and (ii) one or more biological or behavioral features of one or more organisms throughout the one or more trajectories; processing (i) the one or more trajectories of one or more organisms and (ii) the one or more biological or behavioral features of one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms; and training the machine learning model to reduce a loss function that quantifies an error associated with the one or more predictions of the one or more phenotypes compared to one or more ground-truth values of the one or more phenotypes. In- 13 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601some embodiments, the one or more trajectories, the one or more biological or behavioral features, the one or more ground-truth values, or any combination thereof, comprises real data, synthetic data, or both.

[0012] In some aspects, the present disclosure provides a system comprising one or more processors independently or collectively configured for predicting one or more phenotypes of one or more organisms by: processing (i) one or more trajectories of the one or more organisms and (ii) one or more biological or behavioral features of the one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms. In some embodiments, the system is further configured for processing (iii) a second set of one or more phenotypes of a second set of one or more organisms to generate the one or more predictions of the one or more phenotypes of the one or more organisms.

[0013] In some aspects, the present disclosure provides a computer program product comprising computer-executable instructions for: (a) receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects; (b) processing the synthetic data to train a machine learning (ML) model to yield a trained ML model; (c) using the trained ML model to predict a plurality of attributes of a physical environment; and (d) determining, based at least on the predicting in (c), one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent.

[0014] In some aspects, the present disclosure provides a computer program product comprising computer-executable instructions for processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories.

[0015] In some aspects, the present disclosure provides a system comprising a digital twin of an agricultural environment comprising a plurality of virtual agents that tracks a plurality of real entities at an entity-level resolution.

[0016] Another aspect of the present disclosure provides a computer program product comprising a computer-readable medium having computer-executable code encoded therein, the computerexecutable code adapted to be executed to implement any one of the methods or systems disclosed herein.

[0017] Another aspect of the present disclosure provides a non-transitory computer-readable storage media encoded with a computer program including instructions executable by one or more processors to implement any one of the methods or systems disclosed herein.- 14 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0018] Another aspect of the present disclosure provides a computer-implemented system comprising: a digital processing device comprising: at least one processor, an operating system configured to perform executable instructions, a memory, and a computer program including instructions executable by the digital processing device to perform any one of the methods or systems disclosed herein.INCORPORATION BY REFERENCE

[0019] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which:

[0021] FIG. 1 shows an example high-level schematic of the artificial intelligence (Al) platform.

[0022] FIG. 2 shows an example range of agriculture industries and their markets.

[0023] FIGS. 3A-3B show example illustrations of a poultry virtual agriculture environment (VAE) for generating synthetic data for model training. The VAE can be used to simulate various test cases with a variety of agriculture agents and objects, observed with a variety of sensors. The gathered sensor data can be automatically annotated in a log of the VAE. The log can be used to train artificial intelligence (Al) models on the synthetic data. The model can be deployed to the physical world and used in physical facilities.

[0024] FIG. 4 shows an example VAE (bottom row) replicating the conditions of a physical poultry house with 90 chicks (top row), having dimensions of the poultry house, obstructions to the camera view such as lamps, waterers, feeders, materials used in the facility, and physical restrictions for bird movement seen in the pen.- 15 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0025] FIG.5 shows an example schematic for using synthetic data to train models of the Al platform herein, which can include a sensor-attribute model, an attribute-entity model, and an entity-attribute projection model.

[0026] FIG.6 shows an example schematic for initialization of the virtual agent model and a feedback loop to iteratively update the virtual agent model.

[0027] FIG.7 shows an example virtual agriculture agent (VAA) configured with different attributes. A flock of chickens at different stages of life can be configured with different assigned behaviors and densities in the environment. On the far left, chicks are a few days old and are prescribed a strong tendency to huddle together and stay close. In the middle image, chicks are slightly larger, and the agent parameters are set so birds no longer favor huddling together. The right image shows adult birds at a much later stage of life and lower flock density within the same virtual environment. Behaviors can be determined at any arbitrary point in time in the life of the agents.

[0028] FIG. 8 shows an example process of image stitching for multiple cameras.

[0029] FIG. 9 shows an example high-level process or flow chart illustrating VAE usage for model development.

[0030] FIG. 10 shows an example process of training and using a sensor-attribute model to determine phenotype with data from the virtual environment (Left) and the physical environment (Right).

[0031] FIG. 11 shows an example process of training and using an attribute-entity model with data from the virtual environment (Left) and the physical environment (Right). The physical environment can provide unlabeled, unassociated attributes (e.g., through a pre-processing step involving an attribute identification model). The model can then output associated entity IDs for each observed attribute. In the virtual environment, the VAE can automatically provide global log values for these associated IDs, which can be used to train the model automatically.

[0032] FIG. 12 shows an example process of training and using the entity-attribute projection model with data from the virtual environment (Left) and the physical environment (Right). The physical agriculture environment can be used to provide unlabeled data time-histories. The model can then output projected future attributes over a given time horizon. In the virtual environment, the VAE can automatically provide values from the log regarding future attributes, which can be used to train the model automatically. These future values can be predicted based on the physical environment based on unlabeled time histories.- 16 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0033] FIG. 13 shows an example deployment of the Sensor Log to Agent / Object Log model to a physical environment.

[0034] FIG. 14 shows an example process for optimizing the hardware and sensor setup in a physical environment through iterations with varying setups. The optimization loop can continue until adequate model performance is reached and the optimization converges. At this point, the virtual hardware setup can be replicated in the physical environment. Beginning on the left, the physical environment can be reconstructed virtually, and virtual sensors can be installed following an initial, human-determined setup. A virtual experiment can be run where data is collected and used to train the models. The performance of these models can be assessed. Then, given this performance, the virtual sensor setup can be modified to observe agent behavior better and mitigate edge cases where model performance is poor. This loop can be repeated as many times as necessary to ensure the optimal sensor setup. The optimization can be constrained by physical hardware requirements and costs / number of sensors. Once adequate performance is achieved, the virtual sensor setup can be installed at the physical environment.

[0035] FIG. 15 shows an example of an active learning process of improving the VAE and the VAA to emulate the real environment and provide improved Al models to implement on the real environment.

[0036] FIG. 16 shows an example computer system configured to perform methods herein.

[0037] FIG. 17 shows example various workflows for a user interface for monitoring.

[0038] FIG. 18 shows an example high-level workflow of the Al platform.

[0039] FIG. 19 shows an example approach for training models (Left) and tracking performance (Right) of 10 simulations of 10 chickens over 1 day in a VAE using the Al platform versus comparative methods. Data from a virtual poultry environment comprises automatically annotated images and known bird trajectories, which are used to train an Al chicken sensor-attribute model, and an Al chicken attribute-entity model. The performance of 10 simulations with 10 chickens over 1 day in the VAE using the model is compared to YOLOv8™ + SORT™ in the plot on the right. Solid lines indicate the mean performance between all 10 simulations, and dashed lines indicate the minimum and maximum performances.

[0040] FIG. 20 shows an example schematic of a generative model having a transformer architecture, in accordance with some embodiments. The generative model solves three common issues experienced by deterministic models, such as Simple Online and Realtime Tracking (SORT): accumulated loss of position, complete loss of chicken position, and misidentification. The model’s input are tokens of- 17 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601chicken ID, past positions, and the SORT current position, which it then uses to predict chicken ID and position.

[0041] FIG. 21 shows an example high-level workflow of the Al platform.

[0042] FIG. 22 shows an example feedback loop for dynamic and continuous iteration of the Al platform.

[0043] FIG. 23 shows an example graphical representation of quantitative goals of implementing the Al platform.

[0044] FIG. 24 shows an example of a method of digitally twinning a farming operation at an individual entity-resolution. In the context of birds, bird-resolution phenotyping can accelerate breeding / health R& D, while curating high-res datasets for future digital twins. The technology can be similarly applied to other livestock as well as in the context of preclinical mice trials.

[0045] FIG. 25 shows an example of individual- entity level trajectory generation and phenotyping. Trajectories can connect phenotypes and link them to unique entities, building a phenotypic log for breeding / health decisions (where ID association may be used, e.g. RFID or visual identifiers) or commercial digital twin training (where ID association may not be required).

[0046] FIGS.26A-26C show examples of graphical user interfaces. As FIG.26A shows, a graphical user interface can provide a suite of analytical insights, present a record of all bird locations, region densities, activity, events, and / or actionable alerts (e.g. down birds or stress events), among other statistics. As FIG.26B shows, a graphical user interface can display a bird phenotypic log, which can visualize each bird’s phenotypic recordings (e.g. ADG4827) in a facility, from feed / drinking times, location, activity / health, egg events, mating events, weight scores, feather scores, or any combination thereof, as well as trajectories that link an event to the right bird. As FIG.26C shows, a graphical user interface can provide a suite of further analytical insights, presenting flock statistics: total birds, average health score, at-risk birds, eggs laid today, average weight, high performers; individual bird health trends, health distribution, facility efficiency, production rate, risk level percentage, average distance walked per bird, health category distribution, weight distribution, average daily health activity, and egg production by health category.

[0047] FIG. 27 shows an example a technology stack. The stack can comprise: (1) Precision Poultry Tracker — a poultry-specific Kalman-filter tracking algorithm delivering per-bird positioning and identity; (2) Active Learning Fine-Tuning — a Bayesian-optimization loop that identifies extreme parameter settings and continuously refines model performance; (3) Synthetic Data Generation — A- 18 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Peck™ virtual environment that simulates diverse flock scenarios with automatic, pixel-perfect annotations; and (4) Commercial Scale Optimization — topology-aware compute balancing and deployment across real-world facilities.

[0048] FIG. 28 shows an example of a computer vision system deployed for poultry health monitoring. Progressive trials using side- and overhead-mounted camera systems were used demonstrate scalable activity tracking across broiler and breeder environments. From early proof-of-concept (Sasso broilers) to 24 / 7 management experiments and 100-bird livability trials, these deployments validated individual-level monitoring — including under low-light conditions using night vision.

[0049] FIG. 29 shows an example of an automated growth and welfare monitoring system. Left: Example overhead views of diet-restricted / unrestricted with integrated scales. Center left: Weight trajectories (diet vs. unlimited feed) against expected growth. Center right: Daily trajectories showing length vs. time, with low-activity health outliers. Right: Corresponding images confirming a broken leg and a mortality event.

[0050] FIGS.30A-30D show an example of an integrated multi-camera and RFID monitoring system for individualized poultry tracking. FIG. 30A shows eight PoE cameras mounted at 10 feet to ensure complete visual coverage, overlapping across scratch and slat zones. FIG. 30B shows RFID readers placed along the female feed track and pen perimeter to allow identity-linked activity monitoring.FIG. 30C shows a system architecture integrating camera and RFID data through a PoE+ switch, CPU, and dedicated storage, supported by dual UPS units for reliability. FIG.30D shows an example video feed from the 8-camera configuration that shows consistent visual tracking across behavioral zones.

[0051] FIGS.31A-31C show an example filter for tracking an animal. FIG.31A shows a customized, poultry specific tracking loop. FIG. 31B shows 20 representative trajectories from a 100-bird flock over four hours. FIG.31C shows ID-associated (e.g. RFID) trajectories for three birds.

[0052] FIG.32 shows an example active learning loop for learning a filter. A Bayesian-Optimization Active Learning Loop for Poultry-Specific Kalman Filter Tuning was used. Starting from an initial set of parameter-performance pairs (θᵢ, Jᵢ), a machine learning surrogate was trained to model tracking performance across the Kalman-filter parameter space. An acquisition function balances exploration and exploitation to identify “extremely” well-performing candidates. These parameters were then- 19 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601deployed on real flock trajectories, their measured performance fed back into the surrogate, and the loop iterated until convergence on θopt, delivering robust, facility-agnostic tracking.

[0053] FIG. 33 shows an example active learning system for precision chicken identification. Left: Real-world frames were run through two models and the loU difference between their predicted bird masks flags annotation mismatches (highlighted in dark / light shades). These high-discrepancy images were routed to human annotators for correction and then added back into the training set. Right: Synthetic scenes were evaluated by the model to generate error maps; images with the largest prediction errors were automatically selected and injected into the dataset, closing the loop without manual intervention to capture difficult scenarios.

[0054] FIG. 34 shows an example Virtual Agriculture Environment (VAE) pipeline using Peck™. The VAE generated richly diverse synthetic data — varying bird morphology and plumage, litter and pen textures, lighting scenarios (shadows, glare), and high-density crowds — while producing automatic, ground-truth annotations. These annotated scenes seamlessly integrate into our training set, allowing rapid coverage of rare and challenging cases without manual labeling.

[0055] FIGS. 35A-35C show an example camera array and use. FIG. 35A shows a custom low-resolution, multi-camera array (5 cameras) with embedded Raspberry Pi edge compute performs initial frame differencing to pre-filter input before GPU inference. FIG. 35B shows topology toggling via a dynamic graph of chicken centroids: edges colored black (1 FPS) versus red (25 FPS) indicate structural change, enabling per-region frame-rate adaptation, improved occlusion handling, and enhanced Kalman-filter robustness. FIG.35C shows pixel-difference extraction at a 0.2 down-sample factor isolates only high-change regions (shaded boxes), reducing pixel throughput to the object-ID model while preserving detection accuracy.

[0056] FIG. 36 shows an example layout for high-resolution tracking in a 5,000 square foot facility, capable of holding up to 5,000 birds. Layout shows the integrated infrastructure for the proposed experiment, including IP cameras, a custom Pi System cameras, RFID, environmental sensors, feed / water lines, and weight scales. The camera layout — captured in both top-down and transverse views — demonstrates full spatial coverage to enable continuous individual-level behavior and health tracking within commercial-scale housing.

[0057] FIG. 37 shows an example embodiment of an entity-tracking and health monitoring system. Biological states (health or active versus unhealthy or inactive) can be detected by analyzing the trajectories.- 20 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0058] FIG. 38 shows an example analytics of a log from an entity-tracking and health monitoring system. Various statistics of the entities can be analyzed, e.g., distribution of the entities’ activities.

[0059] FIG. 39 shows an example analytics of a log from an entity-tracking and health monitoring system. Various statistics of the entities can be analyzed, e.g., high dimensional data of various features can be measured and / or observed.

[0060] FIG. 40 shows an example of an activity that can be detected. In the context of birds, bird laying time, location, and number of eggs laid can be tracked and logged.

[0061] FIG. 41 shows an example workflow for generating an Al model to phenotype entities based on images of the entities. Human annotators can generate phenotype labels of a fraction of the entities in a system (e.g., about 20%). Sensor data of the entities can be used by a multi-camera system to track the entities in a video and extract images of the entities. The images of the entities, and the phenotype labels, can be used to train a machine learning algorithm to automatically assign phenotypes to unlabeled entities in the system.

[0062] FIG. 42 shows an example of visual identifiers that can be used to tag entities. A visual identifier can comprise a code that is detectable by an image processing algorithm. The tags can be used by a tracking technology.DETAILED DESCRIPTION

[0063] While various embodiments of the present disclosure have been shown and described herein, such embodiments are provided by way of example only. Numerous variations, changes, or substitutions may occur without departing from the present disclosure. It should be understood that various alternatives to the embodiments of the present disclosure described herein may be employed.Overview

[0064] The world population is expected to reach about 10 billion by 2050. To feed the world’s population in 2050, today’s agricultural industry productivity should increase by about 70%. However, projections based on current trends predict that productivity will increase only by 30%. Challenges in scaling the productivity include overcoming labor shortages, adapting to climate change, and controlling for epidemics among crops and livestock.

[0065] To provide an example, the US poultry productivity grew by about 25% in the last decade. However, this increase in productivity was accompanied with an increase in poultry mortality of 54% by 2023, which translates to 5.7% of chickens dying before processing. This figure means that 600- 21 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601million birds have deceased, that 6 million metric tons of avoidable CO₂ was emitted, and nearly $3 billion in losses were adsorbed, all without producing food. While various factors contribute to these losses, two likely factors are that (1) poultry are genetically unhealthy, and (2) there is inadequate commercial production management. And the lack of solutions to both factors can be attributed to the lack of enough available human resources to analyze the production environments to monitor the health of the birds. Commercial flock supervisors may be tasked with monitoring hundreds of thousands of birds at once over dozens of sites. It is unsurprising that commercial poultry producers have seen their ESG (environmental social and governance) scores plummet over the years.

[0066] Recognized herein is that, while the agricultural industry has optimized various streams and facets of agricultural production, there still remains significant variability and unpredictability in each agricultural industry. While massive amounts of human resources may be capable of addressing some of these issues, it is not economically feasible or practical to analyze production or research development environments with a sufficient level of granularity to achieve higher production levels or to develop better products (e.g. agricultural genetics). Thus, the present application provides an Al platform for studying production and research environments to reveal untapped resources and insights for productivity and efficiency gains. The Al platform herein can allow users (e.g., farmers or researchers) to achieve greater financial, environmental, and social sustainability from their production or research environments (e.g., farms).

[0067] FIG. 1 shows a high-level schematic of the approach, which involves replicating a physical environment with a virtual environment, training models based on synthetic data generated using the virtual environment, and applying models to make predictions on the physical, or “real”, environment.

[0068] While various embodiments and applications of the Al platform are disclosed herein, one example is given here for the poultry industry. The Al platform can involve using a number of cameras to obtain images of birds in a production facility from various angles, thereby tracking and monitoring the movement and behavior of the birds. However, collecting information and data from the real environment to train Al models alone does not lead to accurate Al models to track or monitor the birds, making it difficult to identify the health status of the birds. Thus, the Al platform can involve using a virtual agriculture model (VAE), which can be a digital representation of the production facility and its contents (e.g., living organisms and non-living objects) that simulate the behavior of the birds at an individual scale and at an aggregate scale. Moreover, the VAE can simulate the signals or observations that various sensors (e.g., cameras, thermometers, etc.) can pick up at a production facility. Using the- 22 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601VAE, large volumes of high quality of synthetic data can be produced. The synthetic data can be linked to the real-life physical environment of a production facility via the actual sensor data from the production facility. Thus, the synthetic data (1) can be verified, or disputed and the VAE adjusted, and (2) can be used to gain insights into “blind spots” of the positioning of sensors or the capabilities of the Al models, both in terms of obscured sensing (e.g., blind spots of cameras) and obscured biology (e.g., behavioral patterns of birds being a manifestation of its biological status such as its genetics).

[0069] Moreover, video cameras generate enormous amounts of data, so storing all of the data can incur significant data transmission and storage costs, and furthermore, analyzing the video data can be so computationally expensive that real-time analysis of all cameras at once may be undesirable. The data transmission and storage costs can be reduced by converting the data into compressed representations via Al, previously trained on synthetic data, (e.g., high frame rate videos into lower frame rate, point observations of real agents) to reduce the size of the data. The compressed representations of the data can be transmitted to the cloud, where further Al models herein can analyze the data to perform various analyses disclosed herein.

[0070] As mentioned above, this approach provides a significant advantage in that it can allow users to “view” the blind spots of sensors and current Al models. While production facilities can employ numerous cameras and off-the-shelf Al, unusual situations can still arise when camera vision is hampered by unforeseen edge cases, obstructions, etc., and the Al models can fail unless trained on tremendous amount of data. This approach permits the generation of synthetic data of any edge case to better train the Al models and close any performance gaps.

[0071] Another advantage is that the approach can allow users to gain insight into the underlying biology that leads to healthier birds. Even if the birds have identical genomes, significant variations in their environment and epigenetics and the myriad of yet to be understood other biological and behavioral features make it challenging to identify birds that provide better yields. For example, a bird may appear perfectly healthy but be a carrier for a pathogen, and so, even outward behavioral patterns of a bird may not correlate with the health and safety of all of the birds in a facility. Thus, in the VAE, where a user can access hidden factors of a virtual bird’s biology and behavioral patterns (these features matching those that of a real bird), the user can analyze and obtain insights into the difficult to identify patterns that lead to a more productive facility. Thus, users can change the environment, follow a recommended management plant, or other environment design choices that lead to better performance.- 23 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0072] The approach is important for increasing productivity and efficiency for farms, and securing the food security for the future, but that is not all. It is well-known that farms employ large quantities of antibiotics and pesticides, which has significant effects on the environment and significant risks to the health and safety of livestock and humans alike. Heavy utilization of antibiotics breeds drug resistant bacteria, and said drug resistant bacteria may be pathogenic to human consumers. The extensive use of antibiotics in livestock farming generates antibiotic-resistant bacteria that can potentially transmit to humans through the food chain, posing a threat of infecting humans. Using the Al platform disclosed herein, the usage of antibiotics can be controlled, or at the very least, allow users to use antibiotics with more precision and granularity that reduces the risk of breeding super bacteria. More generally, users can create management plans, manually or automatically, based on the analytical insights of the Al platform to improve productivity and efficiency of real facilities. Users can also design a real facility using the Al platform to try out various configurations and analyze how real agents interact with those configurations, and then producing a blueprint of the real facility based on the analysis. Other than bacteria, viruses also pose a threat of spreading to humans. A recent bird flu pandemic was responsible for hundreds of millions of birds lost. This virus has been found to spread from poultry to cattle, ringing alarms that it can also transmit to other livestock or humans.

[0073] The VAE may be a crucial factor in bypassing the data problem in the agricultural industry for these sorts of applications. In many fields, the success of an artificial intelligence approach is often predicated on the availability of big data or the ability create big data with ease. As the nature of the agricultural problem is rooted in biology and interactions of living animals, it might have appeared to some that there will never be enough data to successfully employ model to improve efficiencies at the level of specific granularity described. And so, using the VAE may be a crucial factor in studying agricultural problems from both an individual (livestock) scale as well as emergent phenomena arising from aggregate of behaviors.

[0074] As the present application will further describe in greater detail, the Al platform of the present application can lead to a variety of applications that improve the productivity and efficiency of farms. In some cases, the Al platform can be used to design a farm (e.g., in a “sandbox” mode), allow users to simulate the farm, and understand the impacts of their design choices on the relevant metrics such as productivity or efficiency. In some cases, the Al platform can be used to study epidemiological phenomenon, which can allow user to make choices in designing the farm, determining the appropriate density of livestock on the farm, and / or prepare protocols for identifying and isolating diseased or- 24 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601carrier livestock or crop. In some cases, the Al platform can be used to reduce the consumption of certain resources such as antibiotics, pesticides, fertilizer, etc. In some cases, the Al platform can be used to create a management plan for the farm. In some cases, a real individual determined to exhibit improved attributes over a baseline virtual agent can allow the Al platform to be used for recommending genetic selection of a real individual. In some cases, the Al platform can be used to monitor health attributes in real time for each real individual. In some cases, the Al platform can be used to design, optimize, or improve a design of the physical environment over a baseline physical environment. In some cases, the Al platform can be used to design, optimize, or improve management practices for the physical environment.

[0075] In some cases, the present disclosure provides systems and methods for generating synthetic data for agriculture using a VAE. The VAE can be populated with virtual agriculture agents, virtual sensors, virtual objects, or any combination thereof. The VAE can be used to run simulations of a particular real-world environments to generate a data log. The data log can contain information about the time history of the VAE and entities therein, e.g., virtual agriculture agents, virtual sensors, virtual objects, or any combination thereof. The synthetic data of these logs can be used to train, test, and / or validate artificial intelligence (Al) models to measure or detect attributes, link attributes to entities, and simulate or predict future attributes of events in physical environments. The VAE and the models can be dynamically improved to accurately model and measure the physical environment. When data of a real, physical environment is available, such as sensor data from a physical environment, a model can be used to predict the entire “real global data log” to derive insights based on an incomplete picture that the data obtained from the physical environment provides. For example, a collection of sensors from a physical environment may not provide enough information to piece together a full picture of everything that occurred in the physical environment during the time that the sensor data was collected, however, a model can be used to “fill gaps” or “project” information based on the real sensor data. Users can access and use insights from the real global data log through a user interface. The user interface can provide recommendations or automate actions / methods to optimize the design process of agricultural facilities, experiments, and operations.

[0076] Systems and methods herein can provide technical solutions for solving at least technical challenges in tracking, monitoring, and managing real agricultural facilities, experiments, and operations at an individual-entity resolution. One challenge in providing individual-entity resolution (e.g., resolution at an individual chicken scale in a poultry farming operation), is in tracking each- 25 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601individual entity’s movement and activity in a real facility. There are technical challenges in tracking individual entities from videos. For example, one challenge may be because individual entities may at times be obscured behind objects or other entities in the videos, or, because tracking requires identifying / following entities across several unique cameras or sensors. Another challenge may be because identifying an individual entity can be a challenging image recognition task for Al algorithms in real settings. Moreover, providing actionable insights (e.g., detecting whether an individual is healthy or ill; detecting whether an individual has preferred phenotypes for the operation or not), also presents challenges in creating models that can provide such insights from the data available.

[0077] Systems and methods of the present disclosure can comprise a precision tracker that generates long trajectories of numerous entities. The precision tracker can comprise models that were built using real-life data, without requiring data from virtual agricultural environments or synthetic data. The models can be trained using the real-life data from real sensors in a real facility. The precision tracker can permit a precise, individual-level phenotyping log at scale to unlock true precision agriculture.

[0078] Systems and methods of the present disclosure can comprise a precision tracker that generates long trajectories of numerous entities at least by using models that were built using virtual agricultural environments and synthetic data. The models can then be fine-tuned or reinforced using real-life data when the models are deployed with real sensors in a real facility. For example, the technology’s tracking and phenotyping capabilities can be further improved with 1) active learning to accelerate models’ fine-tuning across disparate environments and facilities; 2) a synthetic-data engine that slashes manual annotation, continuously improving performance and resolving edge cases; and / or 3) topology-aware optimizations dynamically balance compute loads across large operations. Together, these innovations can permit a precise, individual-level phenotyping log at scale to unlock true precision agriculture and create the foundational data to lead to agricultural digital twins.Methods

[0079] In some aspects, the present disclosure provides a computer-implemented method. In some embodiments, the computer-implemented method can comprise receiving synthetic data generated using a virtual environment. In some embodiments, the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, (iii) virtual objects, or (iv) any combination thereof. In some embodiments, the computer-implemented method comprises processing the synthetic data to train a machine learning (ML) model to yield a trained ML model. In some embodiments, the computer-implemented method comprises using the trained ML model to predict a plurality of attributes of a- 26 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601physical environment. In some embodiments, the computer-implemented method comprises determining, based at least on the predicting, one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent.

[0080] In some aspects, the present disclosure provides a computer-implemented method. In some embodiments, the computer-implemented method can comprise training a ML model. In some embodiments, training the ML model can comprise processing one or more trajectories of one or more organisms. The one or more organisms can be living entities, e.g., one or more living, real, or nonvirtual animals. In some embodiments, training the ML model can comprise processing one or more biological or behavioral features of one or more organisms throughout the one or more trajectories. In some embodiments, training the ML model can comprise training the ML model to generate one or more predictions of the one or more phenotypes of the one or more organisms. In some embodiments, training the ML model can comprise updating the ML model to reduce a loss function. In some embodiments, the loss function quantifies an error associated with the one or more predictions of the one or more phenotypes compared to one or more ground-truth values of the one or more phenotypes. In some embodiments, the one or more trajectories, the one or more biological or behavioral features, the one or more ground-truth values, or any combination thereof, comprises real data, synthetic data, or both. In some embodiments, the one or more trajectories, the one or more biological or behavioral features, the one or more ground-truth values, or any combination thereof, comprises at least real data.

[0081] In some aspects, the present disclosure provides a computer-implemented method. In some embodiments, the computer-implemented method can comprise processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories. In some embodiments, the processing comprises tracking each of the one or more identifiers for each of the one or more organisms. In some embodiments, the method further comprises obtaining one or more biological or behavioral features throughout the one or more trajectories. In some embodiments, the method further comprises assigning the one or more biological or behavioral features to the one or more organisms. In some embodiments, the method further comprises assigning one or more phenotypes to the one or more organisms based on the one or more biological or behavioral features. In some embodiments, a subset of the one or more phenotypes are not available for a subset of the one or more organisms. In some embodiments, the method further comprises generating estimates of the subset of the one or more phenotypes for the subset of the one or more organisms. In some embodiments, the method further comprises combining one or more- 27 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601features along the one or more trajectories. In some embodiments, the one or more features along the one or more trajectories are combined to build one or more models for estimating one or more unavailable features along that trajectory. In some embodiments, the one or more features comprise the one or more identifiers, the one or more biological or behavioral features, the one or more phenotypes, or any combination thereof.

[0082] In some aspects, the present disclosure provides a computer-implemented method. In some embodiments, the computer-implemented method can comprise predicting one or more phenotypes of one or more organisms. In some embodiments, the computer-implemented method comprises processing one or more trajectories of the one or more organisms. In some embodiments, the computer-implemented method comprises processing one or more biological or behavioral features of the one or more organisms throughout the one or more trajectories. In some embodiments, the computer-implemented method comprises generating one or more predictions of the one or more phenotypes of the one or more organisms. In some embodiments, the computer-implemented method comprises processing a second set of one or more phenotypes of a second set of one or more organisms. In some embodiments, the computer-implemented method comprises generating one or more predictions of the one or more phenotypes of the one or more organisms.Systems

[0083] In some aspects, the present disclosure provides a system comprising at least one processor and instructions executable by the at least one processor to cause the at least one processor to perform operations. In some embodiments, the one or more processors are configured to perform the operations independently or collectively. In some embodiments, the operations comprise receiving synthetic data generated using a virtual environment. In some embodiments, the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects. In some embodiments, the operations comprise processing the synthetic data to train a machine learning (ML) model to yield a trained ML model. In some embodiments, the operations comprise using the trained ML model to predict a plurality of attributes of a physical environment and a plurality of attributes of real agents in the physical environment. In some embodiments, the operations comprise determining, based at least on the predicting, one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent.

[0084] In some aspects, the present disclosure provides a system comprising at least one processor and instructions executable by the at least one processor to cause the at least one processor to perform- 28 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601operations. In some embodiments, the one or more processors are configured to perform the operations independently or collectively. In some embodiments, the system is configured for training a machine learning model. In some embodiments, the operations comprise processing one or more trajectories of one or more organisms. In some embodiments, the operations comprise computing algorithms that generate a trajectory. In some embodiments, the trajectory is generated by a vision mode, tracking algorithm, or the combination of both. In some embodiments, the operations comprise processing one or more biological or behavioral features of one or more organisms throughout the one or more trajectories. In some embodiments, the operations comprise processing generating one or more predictions of the one or more phenotypes of the one or more organisms. In some embodiments, the operations comprise updating the machine learning model to reduce a loss function. In some embodiments, the loss function quantifies an error associated with the one or more predictions of the one or more phenotypes compared to one or more ground-truth values of the one or more phenotypes. In some embodiments, the one or more trajectories, the one or more biological or behavioral features, the one or more ground-truth values, or any combination thereof, comprises real data, synthetic data, or both. In some embodiments, the one or more trajectories, the one or more biological or behavioral features, the one or more ground-truth values, or any combination thereof, comprises at least real data.

[0085] In some aspects, the present disclosure provides a system comprising at least one processor and instructions executable by the at least one processor to cause the at least one processor to perform operations. In some embodiments, the one or more processors are configured to perform the operations independently or collectively. In some embodiments, the operations comprise processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories. In some embodiments, the operations comprise processing comprises tracking each of the one or more identifiers for each of the one or more organisms. In some embodiments, the operations comprise obtaining one or more biological or behavioral features throughout the one or more trajectories. In some embodiments, the operations comprise assigning the one or more biological or behavioral features to the one or more organisms. In some embodiments, the operations comprise assigning one or more phenotypes to the one or more organisms based on the one or more biological or behavioral features. In some embodiments, a subset of the one or more phenotypes are not available for a subset of the one or more organisms. In some embodiments, the operations comprise generating estimates of the subset of the one or more phenotypes for the subset of the one or more organisms. In some embodiments, the operations comprise combining one or more- 29 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601features along the one or more trajectories. In some embodiments, the operations building one or more models for estimating one or more unavailable features along that trajectory. In some embodiments, the one or more features comprise the one or more identifiers, the one or more biological or behavioral features, the one or more phenotypes, or any combination thereof.

[0086] In some aspects, the present disclosure provides a system comprising at least one processor and instructions executable by the at least one processor to cause the at least one processor to perform operations. In some embodiments, the one or more processors are configured to perform the operations independently or collectively. In some embodiments, the system is configured for predicting one or more phenotypes of one or more organisms. In some embodiments, the operations comprise processing one or more trajectories of the one or more organisms. In some embodiments, the operations comprise processing one or more biological or behavioral features of the one or more organisms throughout the one or more trajectories. In some embodiments, the operations comprise generating one or more predictions of the one or more phenotypes of the one or more organisms. In some embodiments, the operations comprise processing a second set of one or more phenotypes of a second set of one or more organisms to generate the one or more predictions of the one or more phenotypes of the one or more organisms.Synthetic Data

[0087] In some embodiments, the computer-implemented method further comprises generating the synthetic data. A simulation using the VAE can produce synthetic data. The synthetic data can comprise a log of all or a part of the VAE’s virtual entities, such as virtual agents, virtual sensors, and / or virtual objects. The log can be used as synthetic data to train a model. The log can contain all or a part of the data generated from running the simulation. In some embodiments, the computer-implemented method further comprises using the log to automatically train the ML model. In some embodiments, the automatic training comprises supervised training or unsupervised training. In some embodiments, the synthetic data is associated with attributes of the plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects.

[0088] For example, the log can contain a virtual sensor’s signals and / or observations in the VAE, a virtual agent’s attributes (e.g., behaviors, location, growth, etc.), data about the virtual objects (e.g., how agents interacted with a feeder object during simulation), or any combination thereof. The VAE can be configured to maintain data on all or a part of the virtual entities of the VAE in the log. The- 30 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601synthetic data can comprise information about the position, behavior, state, and any other characteristic of the virtual entities of the VAE. The synthetic data can comprise a log of a plurality of attributes of each virtual sensor, virtual agent, or virtual object. The synthetic data can comprise a log of a plurality of attribute data in the virtual environment. The synthetic data can comprise a log of a plurality of entity data and attribute data in the virtual environment. The synthetic data can comprise a log of the time-history data in the virtual environment. The synthetic data can be associated with attributes of the plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects.

[0089] The models can be trained using the generated synthetic data from the virtual environment and, optionally, the real experimental data from sensors at physical agricultural facilities.

[0090] FIG. 5 shows an example schematic for using synthetic data to train a sensor-attribute model, an attribute-entity model, and an entity-attribute projection model. Data from the VAE can be used to train the models (i.e., the sensor-attribute model, attribute-entity model, and entity-attribute prediction model). That is, the VAE simulation can provide virtual data to train the models. The virtual attributes, agents, and object states can be obtained exactly from the VAE log in this training process. Hence, performance metrics from the model can be easily obtained with the synthetic data, and the model can be retrained until adequate performance is achieved, at which point the model can be tested and / or deployed on physical environment data.

[0091] As FIG. 5 shows, the synthetic data (left box) is used to train models, which are tested on physical environment data. The VAE can then be queried multiple times to produce additional data to retrain this model until adequate performance is achieved. Then, once deployed in the physical environment, model performance can be assessed again. If discrepancies between the physical environment and virtual environment statistics are observed, the VAE and virtual agent can be updated to match the physical environment observations better, thus improving the training tool for the models. This loop can be repeated as many times as necessary until adequate performance is obtained on both synthetic and physical data.

[0092] Performance metrics and threshold performance can depend on the type of model. For the sensor-attribute model or the attribute- entity model, the percentage of correct image classification (with thresholds including, but not limited to, 50%, 80%, 90%, 95%, or more correct classification) or the confusion matrix can be used as a performance indicator. For the attribute-entity model or tracking, time of uninterrupted tracking for a unique entity (with thresholds including, but not limited to, 10 minutes, 1 hour, 1 day, 1 week, or up to the entire lifespan of an entity), or percentage of uniquely- 31 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601tracked / associated entities for a given timeframe (with thresholds including, but not limited to 50%, 80%, 90%, 95%, or more) can be used as a performance indicator. For the entity-attribute projection model, the deviation between projection and actual trajectory can be used, or time until trajectory diverges significantly from the actual trajectory for an entity can be used as a performance indicator.

[0093] If the performance metric thresholds are not met, the model can be retrained using more synthetic data obtained by querying the VAE (either specific or general-purpose data). When any discrepancy, such as visual or in performance, is observed, the VAE and virtual agent parameters can be updated to better match the physical environment.

[0094] The trained model can be comprised of multiple modules. The training of three example modules is described in the following subsections.

[0095] FIG.6 shows an example schematic for initialization of an example virtual agent model and a feedback loop to iteratively update the virtual agent model. Various other types of virtual agents may also be employed in the VAE. In this case, the virtual agent model can be initialized in the VAE, and the process of improving this virtual agent model can be through deployment and observation in a physical environment. Beginning with high-level observation (top left box), the agent can be initialized with hard-coded logic or an Al virtual agent can be trained using data. Next, the virtual agent can create data through a VAE simulation, which can be used to train a sensor-attribute model, which can, in turn, be deployed in a physical environment. In this physical environment, a complete global log can be predicted. Then, data from the predicted log can be used to (a) directly improve the virtual agent model through training on the obtained data; (b) analyze the log to obtain statistics of agent behavior, to be recreated via the logic in the virtual agent model; (c) apply (b), and additionally collect synthetic data to train a leaky-logic model, which can complete the logic-based behavior of the agent with data-driven improvements.Virtual Environment

[0096] In some embodiments, the computer-implemented method further comprises developing the virtual environment to generate synthetic data. In some embodiments, the synthetic data comprises a log of a plurality of attributes of each virtual sensor, virtual agent, or virtual object. A virtual environment can comprise the virtual sensor, the virtual agent, and / or the virtual object. A virtual environment can comprise a plurality of virtual sensors, a plurality of virtual agents, and / or a plurality of virtual objects.- 32 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0097] A virtual environment can be a virtual representation of a real, physical environment. The virtual environment can be general (e.g., a sandbox), or be in the likeness of a specific environment (e.g., a specific building). A virtual environment can represent a physical environment in a digital format.

[0098] FIG. 3 shows an example illustration of a poultry virtual agriculture environment (VAE) for generating synthetic data for model training. The VAE can be used to simulate various test cases with a variety of agriculture agents and objects, observed with a variety of sensors. The gathered sensor data can be automatically annotated in a log of the VAE. The log can be used to train artificial intelligence (Al) models on the synthetic data. The model can be deployed to the physical world and used in physical facilities. The data extracted from the VAE can be used to train a model to track and phenotype chickens. Poultry production is only one example of the use of the VAE, which demonstrates a versatile platform for simulating various agricultural scenarios across various sectors. This includes but is not limited to, precision livestock farming more generally (for example, sheep, goats, pigs, etc.), where the VAE can simulate the behavior and movement patterns of different livestock species in various grazing or confined environments. Models can be trained to monitor animal health, behavior, growth, or any combination thereof, as well as detecting signs of stress or disease early. These models can be used to optimize grazing patterns, feed intake, herd management, improving livestock health and productivity, or any combination thereof, while reducing environmental impact.

[0099] The VAE can be a 3 -dimensional virtual representation of an agricultural facility that matches the characteristics and specificity of physical facilities to generate a variety of environment-specific data. Dimensions, lighting, materials, and textures can all be set and designed to replicate any real-world environment to provide a realistic replica of a facility. Alternatively, the VAE can be used to design facilities from scratch and test new facilities before ever starting physical construction. All physical environment elements can be recreated and rendered with high-fidelity 3D models using a 3D visualization engine.

[0100] In some embodiments, the virtual environment or the physical environment comprises an agricultural development facility, a production facility, or a processing facility for use with livestock, animals, aquaculture, row crops, greenhouse produce, greenhouse horticulture, or other agricultural sectors.- 33 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0101] For example, in the context of poultry production, the VAE can include a poultry house structure (e.g., floors, walls, ceilings), enclosures and sub-enclosures, bedding, waterers and feeders, artificial and natural lighting from various light sources, or any combination thereof. Environmental conditions such as temperature, ventilation, and / or humidity can be specified in this environment. Various elements of the virtual facility and agents placed within it can interact with each other and be affected environmental conditions. Virtual sensors, which can generate data to emulate that of a sensor in an equivalent real-world facility, can be placed in the VAE. Virtual sensors can include virtual cameras, microphones, thermometers, hygrometers, etc. A virtual sensor can be susceptible to virtual noise conditions emulating that of the physical world (i.e., camera distortions, moisture affecting sensors, etc.).

[0102] FIG. 4 shows an example VAE (bottom row) replicating the conditions of a physical poultry house with 90 chicks (top row), having dimensions of the poultry house, obstructions to the camera view such as lamps, waterers, feeders, materials used in the facility, and physical restrictions for bird movement seen in the pen. A brooder pen for chicks has been physically built and reconstructed in a virtual 3D environment. Here, the top row shows three camera views of an actual facility, while the bottom row shows the data generated by virtual cameras placed in the same locations in the constructed 3D virtual environment to replicate the view obtained by the cameras at the physical environment. Meshes are included on surfaces of the 3D environment, and virtual agents are placed in and interact with the virtual environment.

[0103] In some embodiments, the computer-implemented method further comprises using a ML model to generate the virtual environment and the plurality of attributes of the virtual sensors, agents, and / or objects from data of the real sensors, agents, and / or objects received from the plurality of real sensors. In some embodiments, the computer-implemented method further comprises using actual data from the physical environment to iteratively update the trained ML model.

[0104] Building the 3D environment can be performed using a mapping technology. The mapping technology can comprise taking a video of a physical environment to generate a 3D representation of the physical environment in a 3D modeling tool, e.g., Unity™. The VAE can be populated with the appropriate density and arrangement of virtual agents, objects, and / or sensors.

[0105] In some embodiments, the VAE can be used to model dairy production. The VAE can simulate dairy cows’ behavior and health metrics, including feeding, milking, and rest cycles. Data from the VAE can be used to train a model to track cow movements, detect signs of illness or stress, monitor- 34 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601milk production, or any combination thereof. The model can optimize barn layouts, feeding schedules, milking routines, or any combination thereof, to enhance cow comfort and milk yield as well as identifying cows with the best genetics for breeding.

[0106] In some embodiments, the VAE can be used to model aquaculture. The VAE can simulate aquatic environments, including water temperature, pH levels, oxygen concentration, or any combination thereof, to simulate fish growth and behavior in various environments. Data from the VAE can be used to train a model to track fish movements, monitor growth rates, detect early signs of disease, to optimize feeding schedules, manage water quality, determine fish stocking densities, or any combination thereof.

[0107] In some embodiments, the VAE can be used to model greenhouse crops or horticulture. The VAE can simulate greenhouse conditions, including light exposure, temperature, humidity, CO2 levels, or any combination thereof, to optimize plant growth. Data from the VAE can be used to train a model to monitor plant health indicators such as chlorophyll content, leaf turgor, flowering stages, or any combination thereof. Growers can utilize the model to optimize environmental controls, such as light intensity, watering schedules, or both, to enhance plant growth and reduce energy consumption.

[0108] In some embodiments, the VAE can be used to model orchard or vineyard management. The VAE can simulate the growth of fruit trees or grapevines, while considering seasonal changes, pruning practices, pest pressures, or any combination thereof. Data from the VAE can be used to train a model to assess fruit set, growth rates, signs of disease or pest infestation, or any combination thereof to optimize pruning schedules, pest management strategies, harvest timing, or any combination thereof, which can lead to higher fruit quality and yield.

[0109] In some embodiments, the VAE can be used to model agroforestry systems. The VAE can simulate systems with trees integrated among crops or livestock and can be used to examine interactions such as shading, root competition, nutrient cycling, or any combination thereof. Data from the VAE can be used to train a model to monitor tree growth, crop yield, the ecological benefits of agroforestry, or any combination thereof, and help users design and manage systems that maximize productivity, biodiversity, and ecosystem services.

[0110] Various attributes of the VAE can be modified. In some embodiments, the plurality of attributes of the virtual environment comprises (i) environmental attributes of weather, temperature, humidity levels, precipitation, airflow, natural lighting, artificial lighting, soil characteristics, disease- 35 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601pressure, or bedding type, (ii) global attributes of agent density, agent uniformity, or a number of agents, or (iii) interactive attributes of feed type, feed delivery, feed availability, water delivery, water availability, or types of management practices, wherein the types of management practices comprise adjusting one or more parameters of chemical solutions, nutrient solutions, or physical solutions to alter at least one attribute of a real agent. In some embodiments, the plurality of attributes of each virtual sensor or real sensor comprises attributes associated with imaging detectors, radio frequency identification (RFID) detectors, motion detectors, pressure detectors, sound detectors, temperature detectors, humidity detectors, water detectors, chemical detectors, ammonia detectors, carbon dioxide detectors, hydrogen sulfide detectors, nitrogen detectors, or light detectors. In some embodiments, the plurality of virtual agents or real agents comprises animals, plants, fungi, protists, or bacteria. In some embodiments, the plurality of attributes of each virtual agent or real agent comprises attributes associated with weight, size, height, color, appearance, visual properties, activity level, feed conversion ratio, nutrient content, growth rate, growth stage, mortality risk, propensity for disease, health metric, propensity for a type of behavior, stress, or social behavior. In some embodiments, the plurality of attributes for each virtual object or real object comprises attributes associated with shelter structures, shelter enclosures, shelter sub-enclosures, bedding, soil, physical barriers, sensor calibration objects, waterers, feeders, irrigation systems, fans, feeders, light sources, sprayers, tractors, agricultural equipment, or physical implements.

[0111] Experiments can be conducted in a VAE. An experiment can be used to test a VAE on different conditions. An experiment can be a disease management trial that simulates different disease management strategies. An experiment can be a feeding or watering efficiency experiment with different feeding or watering schedules, techniques, devices, or products to determine the most efficient and productive practices to maximize relevant output metrics. An experiment can be climate and environment impact experiment to test the impact of various climate scenarios on crop or animal growth and health, pest pressures, and water availability.

[0112] VAEs can involve various operations. Operations can include management operations, including but not limited to feeding, watering, disease / vaccine management, and chemical applications. In cases of environmental control, operations can include managing temperature, humidity, and light in controlled environments like poultry houses, greenhouses and vertical farms. Planting and harvesting operations can include optimizing timing and methods for planting and harvesting crops.- 36 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0113] In some embodiments, the computer- implemented method further comprises using the method to create a management plan based at least on the one or more actions. In some embodiments, the computer-implemented method further comprises using the method for genetic selection of a virtual agent determined to exhibit improved attributes over a baseline virtual agent or real agent.

[0114] In some embodiments, the computer-implemented method further comprises displaying the one or more actions on a user interface (UI) configured with operable user tools which allow a user to select at least one action of the one or more of actions.Virtual Agent

[0115] In some embodiments, the computer-implemented method further comprises using a stochastic model to generate attributes for each virtual agent. In some embodiments, the attributes comprise visual attributes, behavior attributes, performance attributes, or health attributes. In some embodiments, the attributes comprise activity, body weight, size, feed conversion ratio, growth rate, yield, composition, height, physical abnormality (e.g., limping), disposition to a disease or sudden death syndrome, performance, or any combination thereof. In some embodiments, an initial state of each virtual agent is determined by receiving observations of the plurality of real agents.

[0116] A virtual agent can refer to a computer agent encoded with deterministically or stochastically realized predispositions in a simulation. For example, a virtual agent may have predispositions towards a certain activity level, health, behaviors, growth, or performance properties. The predispositions of virtual agents can be based on the agents’ attributes.

[0117] Virtual agent attributes can be used to describe any and all behaviors, lifetime trajectories, positions, states, or other functions and properties of an agent and can be prescribed uniquely to each agent. Agent attributes can be prescribed with hard-coded deterministic logic or stochastic logic. Agent attributes can comprise attributes that are learned and generated through deterministic or stochastic modeling. The VAE can maintain data on every agent and virtual entity of the VAE in a log. The log can contain all or a part of the information about the position, behavior, state, and any other characteristics of the virtual agents and other elements of the VAE.

[0118] For example, a virtual poultry agent may be programmed with a particular health / activity level predisposition (which can be directly defined), which can impact the distance covered by the bird in the simulation (which can be simulated) over some period of time. A virtual cow can be programmed with a specific milk production capacity that is then stochastically realized through simulation and observation of milk yield. The growth of a tomato plant can be prescribed as a given function of light- 37 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601exposure, temperature, and humidity, which yields a stochastic growth rate in the VAE simulation due to varying environmental conditions. All these attributes, both directly programmed behaviors and the resulting simulated trajectories of the agents, can be saved in a log.

[0119] The virtual agriculture agent can include stochastic entity models (including, but not limited to, hard-coded logic, models trained on synthetic and / or physical data, etc.) that can emulate any entity attribute (e.g., visual, behavioral). Models can include feed-forward neural networks such as convolutional neural networks, transformers, LSTMs, multi-modal neural networks, and Gaussian process regression. Example attributes in poultry include, but are not limited to, birds that vary in visual stature (body weight, height, width, color, features) or behavior (pecking others, visiting feed or water, piling onto one another, flocking, chirping, laying eggs), performance (yield, composition, feed conversion ratio, growth rate) or health (high / low activity, predisposition to disease). Example virtual agents in poultry can eat, sleep, drink, peck, grow, walk, run, jump, and / or interact with other agents (e.g., making friends, mourning deaths, panicking in certain situations, contracting diseases, etc.), become injured, and / or die. Virtual agents can respond to factors including, but not limited to, varied nutrition, management actions, and / or environmental conditions such as temperature, humidity, lighting, and the presence of other nearby agents. Management actions can include control of these environmental conditions, changes in feed and / or watering, control of the density of animals or plants, interventions including separating animals, modifying facility operations, etc.

[0120] FIG.7 shows an example virtual agriculture agent (VAA) configured with different attributes. The internal logic, as well as the physical characteristics of the agents, are modified in each image. A flock of chickens at different stages of life can be configured with different assigned behaviors and densities in the environment. On the far left, chicks are a few days old and are prescribed a strong tendency to huddle together and stay close. In the middle image, chicks are slightly larger, and the agent parameters are set so birds no longer favor huddling together. The right image shows adult birds at a much later stage of life and lower flock density within the same virtual environment. Behaviors can be determined at any arbitrary point in time in the life of the agents.

[0121] A VAE can comprise various numbers of virtual agents. A VAE can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or more different types of virtual agents. A VAE can comprise at most 10, 9, 8, 7, 6, 5, 4, 3, or 2 different types of virtual agents. A VAE can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or more virtual agents. A VAE can comprise at most 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less virtual agents.- 38 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Virtual Sensor

[0122] A virtual sensor can observe or measure the virtual environment, like a real sensor can in a physical environment. A sensor can be, e.g., a camera or a thermocouple. Virtual sensors placed in the VAE can extract data from the VAE as a real sensor can in a physical environment. Virtual sensors can be used to replicate any real-world sensor, including, but not limited to, cameras, microphones, temperature and humidity sensors, flow sensors, hyperspectral imaging, infrared imaging, ammonia sensors, light sensors, GPS tracking sensors, RFID tags, accelerometers, soil sensors, etc.

[0123] Virtual sensors can be used to connect observations from the physical world to the deployed virtual models. Sensors can provide the primary mode of input from a physical environment to a virtual environment. Thus, virtual sensors can be modeled to replicate real sensors closely to achieve better performance when deploying the virtually trained Al in physical environments.

[0124] Virtual objects can be placed in the VAE to, but not limited to, influence or engage with agents or hinder / enhance the capabilities of virtual sensors through features prescribed to the virtual objects. The virtual objects can take the form of feeders or waterers, which, among other features, can have a direct attractive force on animals and directly impact their attributes. The virtual objects can take the form of ventilation fans that control components of the VAE environment, such as temperature, generating regions of states in the environment, such as attraction / repulsion of an agent to an area with warm / cool temperatures.

[0125] To cover the large space of physical environments such as broiler houses, and to mitigate detection hindrance caused by obstacles in the fields of view of sensors, multiple sensors can be used.FIG. 8 shows an example process of image stitching for multiple cameras. Multiple cameras and an efficient on-facility computing setup and algorithm can be used to quickly analyze image data and upload minimal data to the cloud for rigorous tracking analysis. An on-facility camera system and object identification can efficiently convert images to point observations to reduce upload size, while the cloud algorithm performs all complex and iterative tracking analyses.

[0126] A VAE can comprise various numbers of virtual sensors. A VAE can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or more different types of virtual sensors. A VAE can comprise at most 10, 9, 8, 7, 6, 5, 4, 3, or 2 different types of virtual sensors. A VAE can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or more virtual sensors. A VAE can comprise at most 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less virtual sensors.- 39 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Virtual Object

[0127] A virtual object can refer to an object in the VAE that may interact with or impact agents (e.g., a feeder that attracts agents and impacts changes in attributes like body weight) or sensors. A virtual object can be static or be dynamic. A virtual object can obstruct sensors, e.g., a line of view of a sensor. A virtual object can be any object that can be found in a corresponding physical environment. The virtual object can be a feeder, a water dispenser, a light, a door, a fence, an obstacle, or a farming equipment.

[0128] A VAE can comprise various numbers of virtual objects. A VAE can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or more different types of virtual objects. A VAE can comprise at most 10, 9, 8, 7, 6, 5, 4, 3, 2, different types of virtual objects. A VAE can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or more virtual objects. A VAE can comprise at most 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less virtual objects.Physical Environment

[0129] A physical environment can refer to an environment that exists in real life. The physical environment can exist outside of a computer, or the virtual environment. The physical environment can be an environment that a virtual environment is modeled from. The physical environment can comprise a plurality of (i) real sensors, (ii) real agents, or (iii) real objects.

[0130] In some embodiments, the physical environment comprises a plurality of (i) real sensors, (ii) real agents, or (iii) real objects. In some embodiments, the plurality of attributes comprises attributes associated with (i) each virtual sensor or real sensor, (ii) each virtual agent or real agent, or (iii) each virtual object or real object. Various physical environments are contemplated herein. In some embodiments, a physical environment can be a poultry production facility, a controlled environment for growing crops or horticulture, an aquaculture facility, other livestock facilities, open-field agriculture, orchards, vineyards, or any combination thereof. FIG. 2 shows an example range of agriculture industries and their markets. As FIG. 2 provides, there exist a wide range of agriculture and food product industries to which the Al platform of the present disclosure can be applied.

[0131] In some embodiments, a physical environment can comprise an agricultural development facility, a production facility, or a processing facility for use with livestock, animals, aquaculture, row crops, greenhouse produce, greenhouse horticulture, or other agricultural sectors. The physical environment can comprise (i) environmental attributes of weather, temperature, humidity levels, precipitation, airflow, natural lighting, artificial lighting, soil characteristics, disease pressure, or-40 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601bedding type, (ii) global attributes of agent density, agent uniformity, or a number of agents, or (iii) interactive attributes of feed type, feed delivery, feed availability, water delivery, water availability, or types of management practices, wherein the types of management practices comprise adjusting one or more parameters of chemical solutions, nutrient solutions, or physical solutions to alter at least one attribute of a real agent.

[0132] A poultry production facility can be designed to house and manage poultry such as chickens, turkeys, and ducks. The VAE can be configured to simulate the lifecycle of poultry production, which can include feeding, breeding, egg-laying, meat processing, or any combination thereof. The VAE can be configured to simulate environmental (e.g., temperature, humidity, lighting schedules, or any combination thereof) and management conditions (e.g., feed nutrition or disease prevention measures, or any combination thereof) of a poultry production facility.

[0133] A controlled environment, such as a greenhouse, can be designed for growing crops or horticulture. The VAE can be configured to simulate different climate conditions (e.g., temperature, humidity, ventilation, lighting, irrigation, or any combination thereof), crop responses, or both.

[0134] An aquaculture facility can involve the breeding, raising, and / or harvesting of fish, shellfish, and / or aquatic plants. The VAE can be configured to simulate water quality, fish growth, and feeding schedules, or any combination thereof, to optimize aquaculture operations.

[0135] Various livestock facilities are also contemplated herein. A livestock facility can be for cattle, sheep, goats in pasture-based systems, or any combination thereof. The VAE can simulate various environmental conditions, feed nutrition, or any combination thereof.

[0136] An open-field agriculture can involve growing crops in outdoor fields. The virtual environment can simulate soil conditions, weather patterns, crop growth, field design, management thereof, or any combination thereof.

[0137] Orchards and vineyards can involve growing fruit trees and grapevines. The virtual environment can simulate tree and vine growth, pest pressures, climate variations, or any combination thereof.

[0138] Various attributes of the physical environment can be modified. In some embodiments, the plurality of attributes of the physical environment comprises (i) environmental attributes of weather, temperature, humidity levels, precipitation, airflow, natural lighting, artificial lighting, soil characteristics, disease pressure, or bedding type, (ii) global attributes of agent density, agent uniformity, or a number of agents, or (iii) interactive attributes of feed type, feed delivery, feed- 41 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601availability, water delivery, water availability, or types of management practices, wherein the types of management practices comprise adjusting one or more parameters of chemical solutions, nutrient solutions, or physical solutions to alter at least one attribute of a real agent. In some embodiments, the plurality of attributes of each real sensor comprises attributes associated with imaging detectors, radio frequency identification (RFID) detectors, motion detectors, pressure detectors, sound detectors, temperature detectors, humidity detectors, water detectors, chemical detectors, ammonia detectors, carbon dioxide detectors, hydrogen sulfide detectors, nitrogen detectors, or light detectors. In some embodiments, the plurality of real agents comprises animals, plants, fungi, protists, or bacteria. In some embodiments, the plurality of attributes of each real agent comprises attributes associated with weight, size, height, color, appearance, visual properties, activity level, feed conversion ratio, nutrient content, growth rate, growth stage, mortality risk, propensity for disease, health metric, propensity for a type of behavior, stress, or social behavior. In some embodiments, the plurality of attributes for real object comprises attributes associated with shelter structures, shelter enclosures, shelter subenclosures, bedding, soil, physical barriers, sensor calibration objects, waterers, feeders, irrigation systems, fans, feeders, light sources, sprayers, tractors, agricultural equipment, or physical implements.

[0139] Experiments can be conducted in a physical environment. An experiment can be used to test a physical environment on different conditions. An experiment can be a disease management trial that simulates different disease management strategies. An experiment can be a feeding or watering efficiency experiment with different feeding or watering schedules, techniques, devices, or products to determine the most efficient and productive practices to maximize relevant output metrics. An experiment can be climate and environment impact experiment to test the impact of various climate scenarios on crop or animal growth and health, pest pressures, and water availability.

[0140] Physical environments can involve various operations. Operations can include management operations, including but not limited to feeding, watering, disease / vaccine management, and chemical applications. In cases of environmental control, operations can include managing temperature, humidity, and light in controlled environments like poultry houses, greenhouses and vertical farms. Planting and harvesting operations can include optimizing timing and methods for planting and harvesting crops.

[0141] In some embodiments, the computer-implemented method can comprise providing an alert to a user. In some embodiments, the alert can be provided in real-time as events occur in the physical- 42 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601environment. In some embodiments, the alert can provide a notification of a dead or dying animal, unusual behavior of an animal, onset of a disease, or any combination thereof. In some embodiments, the alert can provide a notification of a dead or dying bird, unusual behavior of a bird, unusual behavior of a flock of birds, onset of a disease in a bird, or any combination thereof.

[0142] A physical environment can comprise various numbers of sensors. A physical environment can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or more different types of sensors. A physical environment can comprise at most 10, 9, 8, 7, 6, 5, 4, 3, or 2 different types of sensors. A physical environment can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or more sensors. A physical environment can comprise at most 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less sensors.Real Entity

[0143] In some embodiments, the one or more organisms comprise one or more real entities (e.g., living, non-virtual animals) or one or more real agents. In some embodiments, the one or more organisms comprise one organism. In some embodiments, the one or more organisms comprise a plurality of organisms. In some embodiments, the one or more organisms comprise one or more livestock animals. In some embodiments, the one or more livestock animals comprise one or more birds, one or more swine, one or more cattle, one or more fish. In some embodiments, the one or more organisms comprise one or more plants.

[0144] In some embodiments, the one or more organisms comprise attributes. In some embodiments, the attributes comprise visual attributes, behavior attributes, performance attributes, or health attributes. In some embodiments, the attributes comprise activity, body weight, size, feed conversion ratio, growth rate, yield, composition, height, physical abnormality (e.g., limping), disposition to a disease or sudden death syndrome, performance, or any combination thereof.

[0145] An organism may have predispositions towards a certain activity level, health, behaviors, growth, or performance properties. The predispositions of organisms can be based on the organisms’ attributes. A particular health / activity level predisposition of an organisms can impact the distance covered by the organisms in real life.

[0146] Example attributes in poultry include, but are not limited to, birds that vary in visual stature (body weight, height, width, color, features) or behavior (pecking others, visiting feed or water, piling onto one another, flocking, chirping, laying eggs), performance (yield, composition, feed conversion ratio, growth rate) or health (high / low activity, predisposition to disease). Organisms can respond to- 43 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601factors including, but not limited to, varied nutrition, management actions, and / or environmental conditions such as temperature, humidity, lighting, and the presence of other nearby organisms. Management actions can include control of these environmental conditions, changes in feed and / or watering, control of the density of animals or plants, interventions including separating animals, modifying facility operations, etc.

[0147] A physical environment can comprise various numbers of organisms that are tracked or analyzed. A physical environment can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or more different types of organisms that are tracked or analyzed. A physical environment can comprise at most 9, 8, 7, 6, 5, 4, 3, or 2 different types of organisms that are tracked or analyzed. A physical environment can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or more organisms that are tracked or analyzed. A physical environment can comprise at most 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less organisms that are tracked or analyzed.Real Sensor

[0148] A sensor (e.g., a real sensor) can observe or measure the physical environment. A sensor can be, e.g., a camera or a thermocouple. A sensor placed in the physical environment can extract data from the physical environment. A sensor can include cameras, microphones, temperature and humidity sensors, flow sensors, hyperspectral imaging, infrared imaging, ammonia sensors, light sensors, GPS tracking sensors, RFID tags, accelerometers, soil sensors, etc.

[0149] A sensor can be used to provide data that connects observations of the physical environment and organisms to the models of the physical environment and organisms. Sensors can provide the primary mode of input from a physical environment to a model of the physical environment and organisms.

[0150] To cover the large space of physical environments such as broiler houses, and to mitigate detection hindrance caused by obstacles in the fields of view of sensors, multiple sensors can be used. Multiple cameras and an efficient on-facility computing setup and algorithm can be used to quickly analyze image data and upload minimal data to the cloud for rigorous analysis (e.g. tracking, association, or phenotyping). In some cases, the image data can be first processed locally (e.g., tracking), prior to uploading pre-processed data to the cloud for further analysis. An on-facility camera system and object identification can efficiently convert images to point observations to reduce upload- 44 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601size, while the cloud algorithm performs all complex and iterative analyses (e.g. tracking, association, or phenotyping).

[0151] A physical environment can comprise various numbers of sensors. A physical environment can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or more different types of sensors. A physical environment can comprise at most 10, 9, 8, 7, 6, 5, 4, 3, or 2 different types of sensors. A physical environment can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or more sensors. A physical environment can comprise at most 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less sensors.Real Object

[0152] A real object can refer to an object in the physical environment that may interact with or impact organisms (e.g., a feeder that attracts animals and impacts changes in attributes like body weight) or sensors. A real object can be static or be dynamic. A real object can obstruct sensors, e.g., a line of view of a sensor. The real object can be a feeder, a water dispenser, a light, a door, a fence, an obstacle, or a farming equipment.

[0153] Real objects can be placed in the physical environment which may, but not limited to, influence or engage with organisms or hinder / enhance the capabilities of sensors through of the objects. The objects can be feeders or waterers, which, among other features, can have a direct attractive force on animals and directly impact their attributes. The real objects can be ventilation fans that control features of the physical environment, such as temperature, generating regions of states in the environment, such as attraction / repulsion of an organism to an area with warm / cool temperatures.

[0154] A physical environment can comprise various numbers of real objects. A physical environment can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or more different types of real objects. A physical environment can comprise at most 10, 9, 8, 7, 6, 5, 4, 3, or 2 different types of real objects. A physical environment can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or more real objects. A physical environment can comprise at most 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, or 2 real objects.Machine Learning ModelsTraining, Validating, and Testing Models with the Log

[0155] The log of the VAE, when generated, can be automatically annotated and labeled to construct synthetic data for a model. The log of the VAE can track the state of all or a part of the VAE’s virtual entities, which can be automatically labeled / annotated to make input-output pairs of data or any- 45 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601desired data pair (e.g., sensor to attribute, attribute to an entity, attribute projection). A model can then be trained to infer an input-to-output relationship based on the synthetic data. The information from all of the virtual entities and attributes in the VAE tracked in the log can be used to train a model efficiently and accurately. The VAE can generate data for edge cases where the model performance has yet to see data.

[0156] Various types of models can be trained in this approach. One type of model is a sensor to attribute model, and another type of model is an attribute to attribute model. Sensor-to-attribute models can emulate the sensor input data from the physical environment and predict attributes from this direct input data. Attribute-to-attribute models can provide additional layers of models that predict various attributes. Predictions can include, but are not limited to, other attributes, their unique entity, or future attribute projections. These two types of models can be combined to create a complete Sensor Data Log to Agent / Object Data Log model (SL-AOL).

[0157] FIG. 9 shows an example high-level process or flow chart illustrating VAE usage for model development. First, a simulation run using the VAE (top left) can create a log of all or a relevant part of the information and time histories for the sensors, agents, and objects in the VAE. Using this data, any number of models can be trained, although the figure only describes 3 high-level models. A model can be a sensor to attribute model, which can be trained to take sensor data as input from the virtual sensors in the VAE, and output attributes (e.g., agent attributes) from the sensor data. The log can provide known attributes; thus, the synthetic data can be prepared by automatically annotating the sensor data with the known attributes. A model can be an attribute to entity model, which can be trained to map attributes to unique entities in the VAE. The log can provide known attributes and entities; thus, the synthetic data can be prepared by automatically annotating the data. A model can be an entity-attribute-proj ection model, which can be used to project future attribute values given timehistories. The log can provide knowledge of the full time-histories for all entity attributes. Thus, the model can be automatically trained. All three models, or any number of models, can be trained and used to make predictions independently or in concert, where attributes, attribute-entity associations, and attribute projections can be predicted based only on sensor data inputs. Such a model can be referred to as a sensor log to agent / object log model (SL-AOL). The model can be deployed to physical facilities with physical sensors to predict the unobserved or unmeasured data log.- 46 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Sensor-Attribute Model

[0158] In some embodiments, the trained ML model comprises a sensor-attribute model. The sensorattribute model can be configured to (i) analyze data from the plurality of virtual sensors or real sensors and (ii) determine at least one attribute of the plurality of attributes. In some embodiments, generating the sensor-attribute model comprises receiving sensor data from (i) the plurality of virtual sensors, (ii) the plurality of real sensors, or both (i) and (ii). In some embodiments, generating the sensor-attribute model comprises processing the sensor data to determine attributes of the plurality of virtual agents or real agents. In some embodiments, generating the sensor-attribute model comprises updating the synthetic data for use by the trained ML model based at least on the determined attributes.

[0159] In some embodiments, the computer-implemented method further comprises updating the sensor-attribute model based at least on training the sensor-attribute model with the updated synthetic data. In some embodiments, the computer-implemented method further comprises generating the synthetic data, wherein the synthetic data comprises a log of a plurality of attribute data in the virtual environment. In some embodiments, the computer-implemented method further comprises using the log to automatically train the sensor-attribute model, wherein the automatic training comprises supervised training or unsupervised training. In some embodiments, the trained ML model further comprises an attribute-entity model configured to uniquely associate attributes to (i) each virtual agent, (ii) each real agent, or both (i) and (ii).

[0160] A sensor-attribute model can be trained and / or be fine-tuned to extract or annotate attributes (e.g., phenotypes) from synthetic data (e.g., image-based object identifying model). The object and attribute-identification model can be trained and / or be fine-tuned on general and / or environmentspecific synthetic data (e.g., facility, animal species, age, lighting, and / or hardware). This can allow the automatic annotation of the synthetic data, which can far accelerate and improve training accuracy. This synthetic data can be further augmented with data obtained from the physical environment. Utilizing the synthetic data for training the sensor-attribute model, the model may be used to improve automatic annotation of physical environment data, incorporate specific obstructions in image identification, and / or identify edge cases to mitigate performance issues. A sensor-attribute model can specifically query edge cases by utilizing the virtual environment to simulate difficult identification edge cases and train on complex test cases.

[0161] The training method can be applied to any attribute extracted from a sensor or other phenotypic data observations. This can include learning body weight, height, and other bird features. Knowing- 47 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601the values extracted from the virtual environment log for these features can allow accurate and rapid training of sensor-attribute models. Subtle differences in bird characteristics, observed markings, or other sensor inputs can be learned and then be leveraged to make an accurate attribute-association (e.g., tracker) algorithm that associates phenotypes with unique entities to enhance tracking capability via learning individuality.

[0162] FIG. 10 shows an example process of training and using a sensor-attribute model to determine phenotypes with sensor data from the virtual environment (Left) and the physical environment (Right). In both cases, sensors can extract unlabeled data from the environments, which a sensor-attribute model can then use to predict attributes of entities in the environments. Using the virtual environment data for training can allow the VAE to automatically provide the values for these attributes. In that way, the sensor-attribute model can be trained automatically with known, labeled data to compare with the sensor-attribute model predictions. The trained sensor-attribute model can be deployed in the physical environment.Attribute-Entity Model

[0163] In some embodiments, generating the attribute- entity model comprises receiving determined attributes from the sensor-attribute model. In some embodiments, generating the attribute-entity model comprises applying the determined attributes to (i) the virtual sensors, (ii) the real sensors, or both (i) and (ii). In some embodiments, generating the attribute-entity model comprises processing the determined attributes to determine unique attributes for (i) each virtual agent, (ii) each real agent, or both (i) and (ii). In some embodiments, generating the attribute-entity model comprises updating the synthetic data for use by the trained ML model based at least on the determined unique attributes.

[0164] In some embodiments, the computer-implemented method further comprises updating the attribute-entity model based at least on training the attribute-entity model with the updated synthetic data. In some embodiments, the computer-implemented method further comprises generating the synthetic data, wherein the synthetic data comprises a log of a plurality of entity data and attribute data in the virtual environment. In some embodiments, the computer-implemented method further comprises using the log to automatically train the attribute- entity model, wherein the automatic training comprises supervised training or unsupervised training.

[0165] An attribute-entity model can be trained and fine-tuned to associate extracted attributes to unique entities (e.g., tracker). Once attributes or phenotypes are extracted via the trained sensorattribute model, they can be associated with the correct entity. An attribute-entity model can associate- 48 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601attributes (e.g., phenotypes) with entities, for example, by matching the phenotype with an entity ID number. Entities can represent individuals (e.g., individual animals) and groups of individuals, flocks, or other objects.

[0166] The training of an attribute-entity model be carried out via the virtual agriculture environment. Here, the actual phenotype association can be known exactly in the VAE, which can be used to compare and train the attribute-entity model to associate attributes correctly.

[0167] The attribute-entity model can be considered to be a tracking model. That is, with individual phenotypes obtained via sensor data, such as position, posture, performance, and state of an individual entity (e.g., an agent in the virtual environment), the attribute-entity model can track which individual the phenotype corresponds to, thus tracking the position, posture, performance, and state of an individual at each time and allow tracking or reconstruction of a trajectory for an individual entity. This tracker can take into account information from the current observed phenotypes and previously obtained phenotypic and phenotype association data (i.e., from previous timesteps). Using the VAE can allow fast training and optimization of the tracking algorithm.

[0168] The tracking can be based on any sensors, such as camera-based, computer vision models. Tracking models can track gait score and mobility, feeding and drinking behavior, or other behavior such as stretching and preening. Other methods for automated tracking include using wearable devices fitted on birds. However, relying solely on wearable devices for automated tracking can be inconsistent or require costly systems.

[0169] Various tracking models can be used to track the trajectory of identified objects. The tracking model can use Kalman Filters or other data assimilation techniques. Tracking algorithms can use SORT (Simple Online and Realtime Tracking) or TAM (Track Anything Model), or PIV (Particle Image Velocimetry).

[0170] FIG. 11 shows an example process of training and using an attribute-entity model with data from the virtual environment (Left) and the physical environment (Right). The physical environment can provide unlabeled, unassociated attributes (e.g., through a pre-processing step involving an attribute identification model). The model can then output associated entity IDs for each observed attribute. In the virtual environment, the VAE can automatically provide global log values for these associated IDs, which can be used to train the model automatically.

[0171] The tracking model can track an individual over various time periods. The tracking model can track an individual over a time period of at least 10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 12- 49 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601hours, 1 day, 2 days, 4 days, 1 week, 2 weeks, 1 month, 2 months, 4 months, 1 year, or more. The tracking model can track an individual over a time period of at most 1 year, 4 months, 2 months, 1 month, 2 weeks, 1 week, 4 days, 2 days, 1 day, 12 hours, 4 hours, 2 hours, 1 hour, 30 minutes, 10 minutes, or less. The tracking model can track a plurality of individuals over a time period of at least 10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 12 hours, 1 day, 2 days, 4 days, 1 week, 2 weeks, 1 month, 2 months, 4 months, 1 year, or more. The tracking model can track a plurality of individuals over a time period of at most 1 year, 4 months, 2 months, 1 month, 2 weeks, 1 week, 4 days, 2 days, 1 day, 12 hours, 4 hours, 2 hours, 1 hour, 30 minutes, 10 minutes, or less. The plurality of individuals can be at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, or more individuals. The plurality of individuals can be at most 100000, 90000, 80000, 70000, 60000, 50000, 40000, 30000, 20000, 10000, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less individuals.

[0172] The tracking model can track a plurality of individuals at various densities. The tracking model can track a plurality of individuals at a density of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, or more individuals per 100 m2The tracking model can track a plurality of individuals at a density of at most 10000, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less individuals per 100 m2.

[0173] The tracking model can be resilient to various edge cases, and can be trained to do so through the use of VAE synthetic data and / or physical data. The tracking model can track an individual when a view of the individual is obstructed by an obstacle, image noise, or an unusual individual behavior. The tracking model can track an individual under various environments, such as different flooring and wall materials or changes in lighting conditions. The tracking model can track an individual in the dark.Entity-Attribute Projection Model

[0174] In some embodiments, the trained ML model further comprises an entity-attribute projection model. The entity-attribute projection model can be configured to predict the plurality of attributes at one or more future time periods with a predetermined confidence level. In some embodiments, generating the entity-attribute projection model comprises receiving time- history data from (i) the- 50 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601virtual environment, (ii) the physical environment, or both (i) and (ii). In some embodiments, generating the entity -attribute projection model comprises processing the time-history data to predict an attribute over time for (i) each virtual agent, (ii) each real agent, or both (i) and (ii). In some embodiments, generating the entity-attribute projection model comprises updating the synthetic data for use by the trained ML model based at least on actual attributes or the predicted attributes.

[0175] In some embodiments, the computer-implemented method further comprises updating the entity-attribute projection model based at least on training the entity-attribute projection model with the updated synthetic data. In some embodiments, the computer-implemented method further comprises generating the synthetic data. In some embodiments, the synthetic data comprises a log of the time-history data in the virtual environment.

[0176] In some embodiments, the computer-implemented method further comprises using the log to automatically train the entity -attribute projection model. In some embodiments, the automatic training comprises supervised training or unsupervised training. In some embodiments, the trained ML model comprises each of (i) the sensor-attribute model, (ii) the attribute-entity model, and (iii) the entityattribute projection model. In some embodiments, the computer-implemented method further comprises iteratively updating the trained ML model based at least on processing data from the plurality of virtual sensors or real sensors.

[0177] An entity-attribute projection model can be trained and fine-tuned to make entity-specific attribute projections from virtual and physical data. Trained on time histories of attributes or phenotypes from the virtual environment and fine-tuned on specific real-facility data, an entityattribute projection model can project future phenotypic performance based on a time series of attributes. Phenotypic performance can, for example, include future body weight projections, activity levels, growth rate, feed conversion ratio, growth stage, and / or budding / ripening / harvest time. With extensive training in the VAE, the entity-attribute projection model can take as input phenotype time history and learn to predict phenotypes. The entity-attribute projection model can provide the most likely phenotype future trajectories with a given confidence interval determined from VAE training statistics. Confidence intervals can vary based on the type of phenotype and time horizon for the trajectory projection. Acceptable confidence intervals can allow producers to understand their performance and recommend interventions based on predicted outcomes. For example, a confidence interval of + / - 2 days for flowering time for greenhouse-grown roses can help guide management decisions. Similarly, knowledge with 5% of body weight two weeks ahead of time for a broiler in a- 51 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601poultry house, or average for a flock in a poultry house, can guide harvest time decisions. As real-facility data is gathered, phenotype prediction can be updated for each entity, and the entity-attribute projection model can be fine-tuned given entity and population statistics in an experiment / facility.

[0178] FIG. 12 shows an example process of training and using the entity-attribute projection model with data from the virtual environment (Left) and the physical environment (Right). The physical agriculture environment can be used to provide unlabeled data time-histories. The model can then output projected future attributes over a given time horizon. In the virtual environment, the VAE can automatically provide values from the log regarding future attributes, which can be used to train the model automatically. These future values can be predicted based on the physical environment based on unlabeled time histories.

[0179] An advantage of the VAE is the ability to provide data from the log, in this case in the form of future projections, to train an entity-attribute projection model. The entity-attribute projection model can take in data time histories and can effectively be trained to project attributes at future times based on the current state and time histories. This can include, but is not limited to, time histories of body weight, activity levels (e.g., distance covered over time), feeding and drinking behavior, occurrence of disease or lameness, and display of aggressive behaviors. It can also include environmental variables such as temperature, humidity levels, airflow, and lighting, or population-wide data such as density of individuals, flock / population uniformity, and number of individuals. The entity-attribute projection model can be multivariable and be configured to consider time histories across data sources to project attributes.

[0180] These projections can include individual phenotypes such as body weight, feed conversion ratio, mortality risk, disease or lameness propensity, specific behaviors, etc. The prediction confidence can naturally decrease over longer time horizons, and the interval of possible values can expand. The entity-attribute projection model can learn a probability distribution for each phenotype, which can be updated and fine-tuned with the model.Prediction of the Physical Environment Log

[0181] Once trained in the VAE, the AI platform can be applied to the physical environment sensor data and / or on additionally available physical data to predict attributes. The entire physical environment data log can be predicted, even when the attributes are not known in the physical environment, and only sensor data / the sensor data log is available. The AI platform may also be fine-- 52 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601tuned with previously observed physical environment data and / or on additionally available physical data.

[0182] FIG. 13 shows an example deployment of the Sensor Log to Agent / Object Log model to a physical environment. Sensor data can be gathered from the facility in a real agriculture environment (top left box). This sensor data can be used as input to the sensor-to-attribute model or the attribute-to-entity model. This predicted log can complete the log for the physical environment. The predicted log can comprise both the collected sensor log, and the predicted agent / object logs. A graphical user interface can be used to present data from the log to users. As shown in the bottom right box, if prediction performance is poor, the VAE construction can be iterated upon to recreate the observed issues in the physical environment, and the models can be re-trained until adequate performance is achieved before redeploying to the physical environment.Machine Learning Techniques

[0183] A machine learning model can comprise one or more of various machine learning models. In some embodiments, the machine learning model can comprise one machine learning model. In some embodiments, the machine learning model can comprise a plurality of machine learning models. In some embodiments, the machine learning model can comprise a neural network model. In some embodiments, the machine learning model can comprise a random forest model. In some embodiments, the machine learning model can comprise a manifold learning model. In some embodiments, the machine learning model can comprise a hyperparameter learning model. In some embodiments, the machine learning model can comprise an active learning model.

[0184] A graph, graph model, and graphical model can refer to a method of conceptualizing or organizing information into a graphical representation comprising nodes and edges. In some embodiments, a graph can refer to the principle of conceptualizing or organizing data, wherein the data may be stored in a various and alternative forms such as linked lists, dictionaries, spreadsheets, arrays, in permanent storage, in transient storage, and so on, and is not limited to specific cases disclosed herein. In some embodiments, the machine learning model can comprise a graph model.

[0185] The machine learning model can comprise a neural network comprising various architectures, loss functions, optimization algorithms, priors, and various other neural network design choices. In some embodiments, the machine learning model can comprise a neural network. In some embodiments, the machine learning model can comprise an autoencoder. In some embodiments, the machine learning model can comprise a generative model. In some embodiments, the machine learning- 53 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601model can comprise a variational autoencoder. In some embodiments, the machine learning model can comprise a generative adversarial network. In some embodiments, the machine learning model can comprise a flow model. In some embodiments, the machine learning model can comprise an autoregressive model. In some embodiments, the machine learning model can comprise a diffusion model. In some embodiments, the machine learning model can comprise a neural network with one or more layers. In some embodiments, the machine learning model can comprise a neural network with one or more fully connected layers. In some embodiments, the machine learning model can comprise a neural network with one or more convolutional layers. In some embodiments, the machine learning model can comprise a neural network with one or more message-passing layers. In some embodiments, the machine learning model can comprise a neural network with a bottleneck layer. In some embodiments, a layer may comprise an attention mechanism, a generalized message-passing graph neural network, or both. In some embodiments, a generalized message passing graph neural network comprises a graph convolutional neural network.

[0186] In some embodiments, the machine learning model can comprise a neural network with residual blocks. In some embodiments, the machine learning model can comprise a neural network with attention. In some embodiments, the machine learning model can comprise a neural network with one or more non-linearities. In some embodiments, the machine learning model can comprise a neural network with one or more dropout layers. In some embodiments, the machine learning model can comprise a neural network with one or more batch normalization layers. In some embodiments, the machine learning model can comprise a regression loss function. In some embodiments, the machine learning model can comprise a logistic loss function. In some embodiments, the machine learning model can comprise a variational loss. In some embodiments, the machine learning model can comprise a prior. In some embodiments, the machine learning model can comprise a Gaussian prior. In some embodiments, the machine learning model can comprise a non-Gaussian prior. In some embodiments, the machine learning model can comprise an adversarial loss. In some embodiments, the machine learning model can comprise a reconstruction loss. In some embodiments, the machine learning model is trained with the Adam optimizer. In some embodiments, the machine learning model is trained with the stochastic gradient descent optimizer. In some embodiments, the model learning model hyperparameters are optimized with Gaussian Processes. In some embodiments, the machine learning model is trained with train / validation / test data splits. In some embodiments, the machine learning model is trained with k-fold data splits, with any positive integer for k.- 54 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0187] The machine learning model can comprise a variety of manifold learning algorithms. In some embodiments, the machine learning model can comprise a manifold learning algorithm. In some embodiments, the manifold learning algorithm comprises principal component analysis. In some embodiments, the manifold learning algorithm comprises a uniform manifold approximation algorithm. In some embodiments, the manifold learning algorithm comprises an isomap algorithm. In some embodiments, the manifold learning algorithm comprises a locally linear embedding algorithm. In some embodiments, the manifold learning algorithm comprises a modified locally linear embedding algorithm. In some embodiments, the manifold learning algorithm comprises a Hessian eigen mapping algorithm. In some embodiments, the manifold learning algorithm comprises a spectral embedding algorithm. In some embodiments, the manifold learning algorithm comprises a local tangent space alignment algorithm. In some embodiments, the manifold learning algorithm comprises a multidimensional scaling algorithm. In some embodiments, the manifold learning algorithm comprises a t-distributed stochastic neighbor embedding algorithm (t-SNE). In some embodiments, the manifold learning algorithm comprises a Bames-Hut t-SNE algorithm.

[0188] The types of machine learning models may include deep learning neural network architectures (e.g., convolutional neural networks (CNNs), recursive neural networks (RNNs), long short-term memories (LSTMs), transformers, graph convolution networks (GCNs), graph attention networks (GATs), message-passing neural networks (MPNNs), generative adversarial networks (GANs), physics-informed neural networks (PINNs), other neural network architectures, or any combination thereof) and traditional machine learning (e.g., linear and logistic regression, decision trees, gradient boosting, support vector machines, kNN, and other predictive, classification, and clustering algorithms).

[0189] In some embodiments, the methods of the disclosure further comprise reducing one or more representations using a machine learning model. The terms “reducing”, “dimensionality reduction”, “projection”, “component analysis”, “feature space reduction”, “latent space engineering”, “feature space engineering”, “representation engineering”, or “latent space embedding”, as used herein, generally refer to a method of transforming a given input data with an initial number of dimensions to another form of data that has fewer dimensions than the initial number of dimensions. In some embodiments, the terms can refer to the principle of reducing a set of input dimensions to a smaller set of output dimensions. In some embodiments, the terms can refer to the principle of reducing a set of input dimensions to a set of output dimensions of a same or larger size.- 55 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0190] The term “normalizing”, as used herein, generally refers to a collection of methods for adjusting a dataset to align the dataset to a common scale. In some embodiments, a normalizing method can comprise multiplying a portion or the entirety of a dataset by a factor. In some embodiments, a normalizing method can comprise adding or subtracting a constant from a portion or the entirety of a dataset. In some embodiments, a normalizing method can comprise adjusting a portion or the entirety of a dataset to a known statistical distribution. In some embodiments, a normalizing method can comprise adjusting a portion or the entirety of a dataset to a normal distribution. In some embodiments, a normalizing method can comprise adjusting the dataset so that the signal strength of a portion or the entirety of a dataset is about the same.

[0191] Converting can comprise one or more steps of various conversions of data. In some embodiments, converting can comprise normalizing data. In some embodiments, converting can comprise performing a mathematical operation that computes a score based on a distance between 2 points in the data. In some embodiments, the points in the data can comprise a representation. In some embodiments, the distance can comprise a distance between two edges in a graph. In some embodiments, the distance can comprise a distance between two nodes in a graph. In some embodiments, the distance can comprise a distance between a node and an edge in a graph. In some embodiments, the distance can comprise a Euclidean distance. In some embodiments, the distance can comprise a non-Euclidean distance. In some embodiments, the distance can be computed in a frequency space. In some embodiments, the distance can be computed in Fourier space. In some embodiments, the distance can be computed in Laplacian space. In some embodiments, the distance can be computed in spectral space. In some embodiments, the mathematical operation can be a monotonic function based on the distance. In some embodiments, the mathematical operation can be a non-monotonic function based on the distance. In some embodiments, the mathematical operation can be an exponential decay function. In some embodiments, the mathematical operation can be a learned function.

[0192] In some embodiments, converting can comprise transforming data in one representation to another representation. In some embodiments, converting can comprise transforming data into another form of data with less dimensions. In some embodiments, converting can comprise linearizing one or more curved paths in the data. In some embodiments, converting can be performed on data comprising data in Euclidean space. In some embodiments, converting can be performed on data comprising data in graph space. In some embodiments, converting can be performed on data in a discrete space. In- 56 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601some embodiments, converting can be performed on data comprising data in frequency space. In some embodiments, converting can transform data in discrete space to continuous space, continuous space to discrete space, graph space to continuous space, continuous space to graph space, graph space to discrete space, discrete space to graph space, or any combination thereof. In some embodiments, converting can comprise transforming data in discrete space into a frequency domain. In some embodiments, converting can comprise transforming data in continuous space into a frequency domain. In some embodiments, converting can comprise transforming data in graph space into a frequency domain.

[0193] In some embodiments, reducing can comprise transforming a given input data with any initial number of dimensions to another form of data that has any number of dimensions fewer than the initial number of dimensions. In some embodiments, reducing can comprise transforming input data into another form of data with fewer dimensions. In some embodiments, reducing can comprise linearizing one or more curved paths in the input data to the output data. In some embodiments, reducing can be performed on data comprising data in Euclidean space. In some embodiments, reducing can be performed on data comprising data in graph space. In some embodiments, reducing can be performed on data in a discrete space. In some embodiments, reducing can transform data in discrete space to continuous space, continuous space to discrete space, graph space to continuous space, continuous space to graph space, graph space to discrete space, discrete space to graph space, or any combination thereof.

[0194] The terms “clustering”, “cluster analysis”, or “generating modules”, as used herein, generally refer to a method of grouping samples in a dataset by some measure of similarity. Samples can be grouped in a set space, for example, element ‘a’ is in set ‘A’. Samples can be grouped in a continuous space, for example, element ‘a’ is a point in Euclidean space with distance T away from the centroid of elements comprising cluster ‘A’. Samples can be grouped in a graph space, for example, element ‘a’ is highly connected to elements comprising cluster ‘A’. These terms can refer to the principle of organizing a plurality of elements into groups in some mathematical space based on some measure of similarity.

[0195] In some embodiments, the method further comprises clustering a cohort of representations to determine one or more groups of representations with similar structures, properties, or functions. Clustering can comprise grouping any number of samples in a dataset by any quantitative measure of similarity. In some embodiments, clustering can comprise K-means clustering. In some embodiments,- 57 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601clustering can comprise hierarchical clustering. In some embodiments, clustering can comprise using random forest models. In some embodiments, clustering can comprise boosted tree models. In some embodiments, clustering can comprise using support vector machines. In some embodiments, clustering can comprise calculating one or more N-1 dimensional surfaces in N-dimensional space that partitions a dataset into clusters. In some embodiments, clustering can comprise distribution-based clustering. In some embodiments, clustering can comprise fitting a plurality of prior distributions over the data distributed in N-dimensional space. In some embodiments, clustering can comprise using density-based clustering. In some embodiments, clustering can comprise using fuzzy clustering. In some embodiments, clustering can comprise computing probability values of a data point belonging to a cluster. In some embodiments, clustering can comprise using constraints. In some embodiments, clustering can comprise using supervised learning. In some embodiments, clustering can comprise using unsupervised learning.

[0196] In some embodiments, clustering can comprise grouping based on similarity. In some embodiments, clustering can comprise grouping based on quantitative similarity. In some embodiments, clustering can comprise grouping based on one or more features of each sample. In some embodiments, clustering can comprise grouping based on one or more labels of each sample. In some embodiments, clustering can comprise grouping based on Euclidean coordinates. In some embodiments, clustering can comprise grouping based the features of the nodes and edges of each sample.

[0197] In some embodiments, comparing can comprise comparing between a first group and different second group. In some embodiments, a first or a second group can each independently be a cluster. In some embodiments, a first or a second group can each independently be a group of clusters. In some embodiments, comparing can comprise comparing between one cluster with a group of clusters. In some embodiments, comparing can comprise comparing between a first group of clusters with second group of clusters different than the first group.

[0198] In some embodiments, loss functions can implement weights to bring attention to and learn information related to selected samples. These coefficients may take the form of enhancing the understanding of rare and extreme events, discrete quantities of interest, or to inform learning that minimizes error to other desired metrics.Physical Environment Design- 58 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0199] In some embodiments, the operations comprise creating a management plan based at least on the one or more actions. In some embodiments, the operations comprise performing genetic selection of a virtual agent or a real agent determined to exhibit improved attributes over a baseline virtual agent or real agent. In some embodiments, the operations comprise monitoring health attributes in real time for each real agent. In some embodiments, the operations comprise designing, optimizing, or improving a design of the physical environment over a baseline physical environment. In some embodiments, the one or more actions comprise adjusting (i) environmental parameters of the physical environment, (ii) feeding, watering, or other management parameters for the plurality of real agents, or (iii) density of the plurality of real agents. In some embodiments, the operations comprise using a ML model to generate the virtual environment and the plurality of attributes of the virtual sensors, agents, and / or objects from data of the real sensors, agents, and / or objects received from the plurality of real sensors. In some embodiments, the operations comprise using actual data from the physical environment to iteratively update the trained ML model.

[0200] The AI platform can be used for general facility and environment design. That is, it can be used for optimal design of production facilities via virtual facility optimization. Before building a facility, designs can be iterated using the virtual space to save costs and optimize the facility for specific goal outputs (e.g., optimize revenue, body weight, feed conversion ratio). Parameters that can be optimized through this process include (but are not limited to) physical environment specificity, breed / species used, lighting / heating / ventilation system design, and other environmental variables.

[0201] One such use case is the design of experiments and experimental facilities, where the effect of input parameters (e.g., environmental, management, etc.) on given output metrics (e.g., health metrics, production output, etc.) is to be studied. Experimental design can be carried out virtually before any physical experimental facility is built.

[0202] The AI platform can be used to optimize hardware and sensor setup for optimal phenotyping performance. While optimizing a design for a facility, the virtual agriculture environment can allow the optimization of the hardware and sensor setups for maximum phenotyping / monitoring system performance. Through iterations in the virtual environment, hardware and sensor placement can be optimized to achieve the best performance with the phenotyping, tracking, and health monitoring models in a facility.

[0203] FIG. 14 shows an example process for optimizing the hardware and sensor setup in a physical environment through iterations with varying setups. The optimization loop can continue until adequate- 59 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601model performance is reached and the optimization converges. At this point, the virtual hardware setup can be replicated in the physical environment. Beginning on the left, the physical environment can be reconstructed virtually, and virtual sensors can be installed following an initial, human-determined setup. A virtual experiment can be run where data is collected and used to train the models. The performance of these models can be assessed. Then, given this performance, the virtual sensor setup can be modified to observe agent behavior better and mitigate edge cases where model performance is poor. This loop can be repeated as many times as necessary to ensure the optimal sensor setup. The optimization can be constrained by physical hardware requirements and costs / number of sensors. Once adequate performance is achieved, the virtual sensor setup can be installed at the physical environment.

[0204] The method can involve recreating a facility as a VAE, including an initial virtual hardware outfit installation. Then, synthetic data is gathered, and a model is trained, the performance of which can be benchmarked (given knowledge of the attributes of virtual agents, the model performance is exactly known). Given this performance, the hardware setup can then be modified to address issues and edge cases in the model performance, and a new experiment can be run in the virtual facility with new virtual sensors. This optimization can be repeated until adequate performance is achieved and the determined set of hardware can be installed in the physical environment. This optimization can easily be constrained to only physically possible sensor locations, hardware costs, sensors' total number, etc.Active Learning

[0205] The virtual environment and the agent can be updated as more physical environment data is collected and translated to the virtual environment. The model can constantly improve performance with each iteration in this active-learning feedback loop.

[0206] FIG. 15 shows an example active learning process. Starting on the left, an initial version of the virtual agriculture agent (in this case, a chicken with a given set of behaviors and characteristics, first initialized via hard-coded logic to replicate human-observed behaviors) can be used. This agent can be placed in the VAE, a simulation can be run, and data can be captured. From this captured data, improved attribute identification, association, and prediction models can be trained and deployed in the real-world facility. There, by observing the statistics of individuals (and their attributes obtained via the models) from the physical environment, the virtual agent can be updated, as well as the models used. With this newly updated agent, the cycle can be restarted, with incremental increases in performance with each loop.- 60 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0207] In some embodiments, active learning can comprise Bayesian optimization (BO), Bayesian experimental design (BED), BO- or BED-inspired algorithms, or other sequential learning algorithms.

[0208] In some embodiments, active learning can implement custom weights to bring attention to and learn information related to samples to be queried from the VAE or physical environment. These coefficients may take the form of enhancing the understanding of rare and extreme events (i.e. rare event active learning (R. E. A. L.), discrete quantities of interest, or to inform learning that minimizes error to other desired metrics.Trajectories

[0209] In some embodiments, a trajectory comprises a movement of an entity through space. In some embodiments, the space is an agricultural environment, e.g., a room, a cage, or a floor. In some embodiments, the movement is walking, sprinting, running, flying, etc. In some embodiments, the one or more trajectories comprise one trajectory. In some embodiments, the one or more trajectories comprise a plurality of trajectories. In some embodiments, each of the one or more trajectories comprise a discrete trajectory of a discrete organism of the one or more organisms.

[0210] In some embodiments, the one or more trajectories are at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, or 55 minutes long. In some embodiments, the one or more trajectories are at most 55, 50, 45, 40, 35, 30, 25, 20, 15, 10, or 5 minutes long. In some embodiments, the one or more trajectories are at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours long. In some embodiments, the one or more trajectories are at most 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less hours long. In some embodiments, the one or more trajectories are at least 1, 2, 3, 4, 5, or more weeks long. In some embodiments, the one or more trajectories are at most 1, 2, 3, 4, 5, or less weeks long. In some embodiments, the one or more trajectories are at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or more months long. In some embodiments, the one or more trajectories are at most 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less months long.

[0211] In some embodiments, the obtaining the one or more trajectories comprises receiving, generating, or measuring the one or more trajectories. In some embodiments, the one or more trajectories can be obtained, e.g., through the use of one or more sensors. In some embodiments, the measuring is performed using vision tracking, visual identifiers, ultra-high or high frequency RFID, a lidar, or any combination thereof. In some embodiments, the measuring is performed using a plurality of cameras. In some embodiments, the measuring is performed using image segmentation based on- 61 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601the plurality of cameras. In some embodiments, the one or more trajectories comprise one or more 2D or 3D trajectories.

[0212] In some embodiments, the one or more trajectories are measured from an environment comprising one or more obstructions or obstacles. In some embodiments, the one or more obstructions or obstacles comprise: a food source, a water source, a shelter, or any combination thereof.

[0213] In some embodiments, a trajectory can be, itself, a biological feature. For example, various featurizations or derivations of new data from the trajectory (e.g., distance covered, steps, activity score, entry / exit points, etc.) can be used as biological features.Behavioral or Biological features

[0214] In some embodiments, the one or more biological or behavioral features comprise one biological or behavioral feature. In some embodiments, the one or more biological or behavioral features comprise a plurality of biological or behavioral features. In some embodiments, the one or more biological or behavioral features comprise: a feeding event, a drinking event, a urinating event, a defecating event, a mating event, a reproducing event, an injury event, an illness event, a death event, an aggression event, a socializing event, a weight, a height, a movement pattern, a visual, a diagnosis, a prognosis, a symptom, an epidermal feature, or any combination thereof. In some embodiments, the epidermal feature comprises a visual appearance or optical feature of feather, fur, hair, beak, eyes, skin, feet, hooves, claws, or any combination thereof.

[0215] In some embodiments, the obtaining the one or more biological or behavioral features comprises receiving, generating, or measuring the one or more biological or behavioral features. In some embodiments, the measuring is performed using a sensor, a human intervention, a model, or any combination thereof. In some embodiments, the model is a vision to weight model.Identifiers

[0216] In some embodiments, the one or more identifiers comprise one identifier. In some embodiments, the one or more identifiers comprise a plurality of identifiers. In some embodiments, the one or more identifiers can be obtained by receiving, generating, or measuring the one or more identifiers. In some embodiments, the measuring is performed by detecting a visual identifier, a tag, a barcode, RFID, human annotation, or any combination thereof.Assigning Phenotypes

[0217] In some embodiments, the one or more phenotypes comprise one phenotype. In some embodiments, the one or more phenotypes comprise a plurality of phenotypes. In some embodiments,- 62 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601the one or more phenotypes comprise: a health state, an injury state, a disease state, a feather score, a health score, a death state, a disease score, an activity score, feeding time, drinking time, number of eggs laid, time spent in a nest box, total distance traveled, weight, time in scratch area, time on slats, confidence score of any of the preceding, or any combination thereof.

[0218] In some embodiments, the assigning one or more phenotypes is performed using a model. In some embodiments, the model is a deterministic model the processes (i) annotated phenotype data of a first subset of the one or more organisms and (ii) sensor data of a second subset of the one or more organisms, to determine the one or more phenotypes for the second subset of the one or more organisms. In some embodiments, the model is a machine learning model trained to process one or more images to determine the one or more phenotypes.Agricultural Actions

[0219] In some embodiments, an agricultural action may be recommended or taken. In some embodiments, the agricultural action comprises isolating an organism, selecting an organism for breeding or propagating, sorting one or more organisms, washing or cleaning an organism, vaccinating an organism, administering a drug (e.g., an antibiotic) to an organism, removing an organism, culling an organism, or isolating an organism. In some embodiments, an organism may be isolated from the one or more organisms based on a phenotype of the organism. In some embodiments, the phenotype of the organism comprises an infectious disease state, lameness state, or any other unhealthy, nonproductive, or risk inducing state.Computing System

[0220] In some aspects, the present disclosure describes a computer-implemented system comprising: a digital processing device comprising: at least one processor, an operating system configured to perform executable instructions, a memory, and a computer program including instructions executable by the digital processing device to generate synthetic data using a virtual environment, receive synthetic data generated using a virtual environment, process synthetic data to train a machine learning (ML) model, use a ML model to predict a plurality of attributes of a physical environment, determine one or more actions for a user to perform, predict attributes of a real agent or a log or time history of a physical environment. In some aspects, the present disclosure describes a computer-implemented system comprising: a digital processing device comprising: at least one processor, an operating system configured to perform executable instructions, a memory, and a computer program including instructions executable by the digital processing device to measure real data from a physical- 63 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601environment, process the real data using a one or more models to predict a plurality of attributes of the physical environment or one or more organisms therein, or determine one or more actions for a user to perform. In some aspects, the present disclosure describes a computer-implemented method, implementing any one of the methods disclosed herein in a computer system. FIG. 16 shows an example computer system configured to perform methods herein. Referring to FIG. 16, a block diagram is shown depicting an exemplary machine that includes a computer system 1600 (e.g., a processing or computing system) within which a set of instructions can execute for causing a device to perform or execute any one or more of the aspects and / or methodologies for generating synthetic data using a virtual environment, receiving synthetic data generated using a virtual environment, processing synthetic data to train a machine learning (ML) model, using a ML model to predict a plurality of attributes of a physical environment, determining one or more actions for a user to perform, predicting attributes of a real agent or a log or time history of a physical environment. The components in FIG. 16 are examples only and do not limit the scope of use or functionality of any hardware, software, embedded logic component, or a combination of two or more such components implementing particular embodiments.

[0221] Computer system 1600 may include one or more processors 1601, a memory 1603, and a storage 1608 that communicate with each other, and with other components, via a bus 1640. The bus 1640 may also link a display 1632, one or more input devices 1633 (which may, for example, include a keypad, a keyboard, a mouse, a stylus, etc.), one or more output devices 1634, one or more storage devices 1635, and various tangible storage media 1636. All of these elements may interface directly or via one or more interfaces or adaptors to the bus 1640. For instance, the various tangible storage media 1636 can interface with the bus 1640 via storage medium interface 1626. Computer system 1600 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers.

[0222] Computer system 1600 includes one or more processor(s) 1601 (e.g., central processing units (CPUs), general purpose graphics processing units (GPGPUs), or quantum processing units (QPUs)) that carry out functions. Computer system 1600 may be one of various high performance computing platforms. For instance, the one or more processor(s) 1601 may form a high performance computing cluster. In some embodiments, the one or more processors 1601 may form a distributed computing system connected by wired and / or wireless networks. In some embodiments, arrays of CPUs, GPUs,- 64 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601QPUs, or any combination thereof may be operably linked to implement any one of the methods disclosed herein. Processor(s) 1601 optionally contains a cache memory unit 1602 for temporary local storage of instructions, data, or computer addresses. Processor(s) 1601 are configured to assist in execution of computer readable instructions. Computer system 1600 may provide functionality for the components depicted in FIG. 16 as a result of the processor(s) 1601 executing non-transitory, processor-executable instructions embodied in one or more tangible computer-readable storage media, such as memory 1603, storage 1608, storage devices 1635, and / or storage medium 1636. The computer-readable media may store software that implements particular embodiments, and processor(s) 1601 may execute the software. Memory 1603 may read the software from one or more other computer-readable media (such as mass storage device(s) 1635, 1636) or from one or more other sources through a suitable interface, such as network interface 1620. The software may cause processor(s) 1601 to carry out one or more processes or one or more steps of one or more processes described or illustrated herein. Carrying out such processes or steps may include defining data structures stored in memory 1603 and modifying the data structures as directed by the software.

[0223] The memory 1603 may include various components (e.g., machine readable media) including, but not limited to, a random access memory component (e.g., RAM 1604) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phase-change random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 1605), and any combinations thereof. ROM 1605 may act to communicate data and instructions unidirectionally to processor(s) 1601, and RAM 1604 may act to communicate data and instructions bidirectionally with processor(s) 1601. ROM 1605 and RAM 1604 may include any suitable tangible computer-readable media described below. In one example, a basic input / output system 1606 (BIOS), including basic routines that help to transfer information between elements within computer system 1600, such as during startup, may be stored in the memory 1603.

[0224] Fixed storage 1608 is connected bidirectionally to processor(s) 1601, optionally through storage control unit 1607. Fixed storage 1608 provides additional data storage capacity and may also include any suitable tangible computer-readable media described herein. Storage 1608 may be used to store operating system 1609, executable(s) 1610, data 1611, applications 1612 (application programs), and the like. Storage 1608 can also include an optical disk drive, a solid-state memory device (e.g., flash-based systems), or a combination of any of the above. Information in storage 1608 may, in appropriate cases, be incorporated as virtual memory in memory 1603.- 65 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0225] In one example, storage device(s) 1635 may be removably interfaced with computer system 1600 (e.g., via an external port connector (not shown)) via a storage device interface 1625. Particularly, storage device(s) 1635 and an associated machine-readable medium may provide non-volatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for the computer system 1600. In one example, software may reside, completely or partially, within a machine-readable medium on storage device(s) 1635. In another example, software may reside, completely or partially, within processor(s) 1601.

[0226] Bus 1640 connects a wide variety of subsystems. Herein, reference to a bus may encompass one or more digital signal lines serving a common function, where appropriate. Bus 1640 may be any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. As an example, and not by way of limitation, such architectures include an Industry Standard Architecture (ISA) bus, an Enhanced ISA (EISA) bus, a Micro Channel Architecture (MCA) bus, a Video Electronics Standards Association local bus (VLB), a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, an Accelerated Graphics Port (AGP) bus, HyperTransport (HTX) bus, serial advanced technology attachment (SATA) bus, and any combinations thereof.

[0227] Computer system 1600 may also include an input device 1633. In one example, a user of computer system 1600 may enter commands and / or other information into computer system 1600 via input device(s) 1633. Examples of an input device(s) 1633 include, but are not limited to, an alphanumeric input device (e.g., a keyboard), a pointing device (e.g., a mouse or touchpad), a touchpad, a touch screen, a multi-touch screen, a joystick, a stylus, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), an optical scanner, a video or still image capture device (e.g., a camera), and any combinations thereof. In some embodiments, the input device is a Kinect, Leap Motion, or the like. Input device(s) 1633 may be interfaced to bus 1640 via any of a variety of input interfaces 1623 (e.g., input interface 1623) including, but not limited to, serial, parallel, game port, USB, FIREWIRE, THUNDERBOLT, or any combination of the above. In some embodiments, an input device 1633 may be used to generate synthetic data using a virtual environment, receive synthetic data generated using a virtual environment, process synthetic data to train a machine learning (ML) model, use a ML model to predict a plurality of attributes of a physical environment, determine one or more actions for a user to perform, predict attributes of a real agent or a log or time history of a physical environment.- 66 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0228] In particular embodiments, when computer system 1600 is connected to network 1630, computer system 1600 may communicate with other devices, specifically mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, and the like, connected to network 1630. Communications to and from computer system 1600 may be sent through network interface 1620. For example, network interface 1620 may receive incoming communications (such as requests or responses from other devices) in the form of one or more packets (such as Internet Protocol (IP) packets) from network 1630, and computer system 1600 may store the incoming communications in memory 1603 for processing. Computer system 1600 may similarly store outgoing communications (such as requests or responses to other devices) in the form of one or more packets in memory 1603 and communicated to network 1630 from network interface 1620.Processor(s) 1601 may access these communication packets stored in memory 1603 for processing.

[0229] Examples of the network interface 1620 include, but are not limited to, a network interface card, a modem, and any combination thereof. Examples of a network 1630 or network segment 1630 include, but are not limited to, a distributed computing system, a cloud computing system, a wide area network (WAN) (e.g., the Internet, an enterprise network), a local area network (LAN) (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a direct connection between two computing devices, a peer-to-peer network, and any combinations thereof. A network, such as network 1630, may employ a wired and / or a wireless mode of communication. In general, any network topology may be used.

[0230] Information and data can be displayed through a display 1632. Examples of a display 1632 include, but are not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display, a plasma display, and any combinations thereof. The display 1632 can interface to the processor(s) 1601, memory 1603, and fixed storage 1608, as well as other devices, such as input device(s) 1633, via the bus 1640. The display 1632 is linked to the bus 1640 via a video interface 1622, and transport of data between the display 1632 and the bus 1640 can be controlled via the graphics control 1621. In some embodiments, the display is a video projector. In some embodiments, the display is a head-mounted display (HMD) such as a VR headset. In further embodiments, suitable VR headsets include, by way of non-limiting examples, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR,- 67 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Zeiss VR One, Avegant Glyph, Freefly VR headset, and the like. In still further embodiments, the display is a combination of devices such as those disclosed herein.

[0231] In addition to a display 1632, computer system 1600 may include one or more other peripheral output devices 1634 including, but not limited to, an audio speaker, a printer, a storage device, and any combinations thereof. Such peripheral output devices may be connected to the bus 1640 via an output interface 1624. Examples of an output interface 1624 include, but are not limited to, a serial port, a parallel connection, a USB port, a FIREWIRE port, a THUNDERBOLT port, and any combinations thereof.

[0232] In addition, or as an alternative, computer system 1600 may provide functionality as a result of logic hardwired or otherwise embodied in a circuit, which may operate in place of or together with software to execute one or more processes or one or more steps of one or more processes described or illustrated herein. Reference to software in this disclosure may encompass logic, and reference to logic may encompass software. Moreover, reference to a computer-readable medium may encompass a circuit (such as an IC) storing software for execution, a circuit embodying logic for execution, or both, where appropriate. The present disclosure encompasses any suitable combination of hardware, software, or both.

[0233] Those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality.

[0234] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.- 68 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0235] The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by one or more processor(s), or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0236] In accordance with the description herein, suitable computing devices include, by way of nonlimiting examples, server computers, desktop computers, laptop computers, notebook computers, subnotebook computers, netbook computers, netpad computers, set-top computers, media streaming devices, handheld computers, Internet appliances, mobile smartphones, and tablet computers.

[0237] In some embodiments, the computing device includes an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device’s hardware and provides services for execution of applications. Those of skill in the art will recognize that suitable server operating systems include, by way of nonlimiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU / Linux®. In some embodiments, the operating system is provided by cloud computing. Those of skill in the art will also recognize that suitable mobile smartphone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® los®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.

[0238] In some embodiments, a computer system 1600 may be accessible through a user terminal to receive user commands. The user commands may include line commands, scripts, programs, etc., and various instructions executable by the computer system 1600. A computer system 1600 may receive instructions to generate synthetic data using a virtual environment, receive synthetic data generated- 69 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601using a virtual environment, process synthetic data to train a machine learning (ML) model, use a ML model to predict a plurality of attributes of a physical environment, determine one or more actions for a user to perform, predict attributes of a real agent or a log or time history of a physical environment, or schedule a computing job for the computer system 1600 to carry out any instructions. A computer system 1600 may receive instructions to measure real from a physical environment, process the real data with a ML model to predict a plurality of attributes of a physical environment or one or more organisms therein, or determine one or more actions for a user to perform.User Interface

[0239] In some aspects, the present disclosure provides a tool for phenotypic and health monitoring.FIG. 17 shows example various uses of a user interface for monitoring. The user interface can obtain annotated phenotypes and attributes from models, which can be viewed in real-time or analyzed as time histories. The interface can trigger actionable alerts, inform management decisions by viewing predicted phenotypes over time, and / or enable interventions automatically or with human input. Annotated phenotypes and attributes can be viewed in real-time, and the entity or flock-level statistics, both live and for a given time history can be retrieved from the dashboard.

[0240] Real-time phenotypic monitoring can alert and implement actions to resolve issues (e.g., health issues), optimize facility and entity performance, or investigate improved or decreased performance. The user interface can comprise an interactive 3D virtual reconstruction of observed events to investigate precursors that evolve into particular issues, positive or negative. Users can be virtually immersed in a facility over a desired timeframe to analyze, for example, the minutes leading up to an undesired or desired event and witness in 3D the behaviors of their livestock. Alternatively, users can follow an individual known to have particular behavioral traits (e.g., showing signs of improved / poor activity, not eating / drinking, or other health properties) to learn what can lead to (or did lead to) improved or decreased performance (e.g. death).

[0241] Following monitoring alerts, predicted outcomes of management actions to address observed issues can be provided, and suggestions on optimal actions can be provided. Real-time actions through remote hardware (e.g., a robot to remove a dead bird) can also be initiated remotely (either automatically or through the approval of humans) after a monitoring alert is triggered. A robot can be a drone, for example.- 70 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0242] The platform can also provide real-time metrics, accumulated metrics over time, and / or projected metrics of the monitored individuals. Top performers and weak performers who may be approaching critical health events requiring action can be labeled and visualized.

[0243] One example of using this platform is for optimizing harvest time. Early harvest can be triggered if an individual is showing critical health signs. On the other hand, harvest can be postponed for top performers to increase body weight or to align harvest with other factors, taking into account a backend cost / revenue calculation to keep it alive given user-inputted (or automatically web-scraped) feed and operation costs, as well as market prices, contract timelines, demand, processing constraints, and environmental factors.Non-Transitory Computer Readable Storage Medium

[0244] In some aspects, the present disclosure describes a non-transitory computer-readable storage media encoded with a computer program including instructions executable by one or more processors to generate synthetic data using a virtual environment, receive synthetic data generated using a virtual environment, process synthetic data to train a machine learning (ML) model, use a ML model to predict a plurality of attributes of a physical environment, determine one or more actions for a user to perform, predict attributes of a real agent or a log or time history of a physical environment using any one of the methods disclosed herein. In some embodiments, a non-transitory computer-readable storage media may comprise instructions for generating synthetic data using a virtual environment, receiving synthetic data generated using a virtual environment, processing synthetic data to train a machine learning (ML) model, using a ML model to predict a plurality of attributes of a physical environment, determining one or more actions for a user to perform, predicting attributes of a real agent or a log or time history of a physical environment. In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked computing device. In some aspects, the present disclosure describes a non-transitory computer-readable storage media encoded with a computer program including instructions executable by one or more processors to measure real data from a physical environment, process the real data with a ML model to predict a plurality of attributes of a physical environment or one or more organisms therein, or determine one or more actions for a user to perform.

[0245] In further embodiments, a computer readable storage medium is a tangible component of a computing device. In still further embodiments, a computer readable storage medium is optionally- 71 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601removable from a computing device. In some embodiments, a computer readable storage medium includes, by way of non-limiting examples, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, distributed computing systems including cloud computing systems and services, and the like. In some embodiments, the program and instructions are permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media.Computer Program

[0246] In some aspects, the present disclosure describes a computer program product comprising a computer-readable medium having computer-executable code encoded therein, the computerexecutable code adapted to be executed to implement any one of the methods disclosed herein. In some embodiments, the platforms, systems, media, and methods disclosed herein include at least one computer program, or use of the same.

[0247] A computer program includes a sequence of instructions, executable by one or more processor(s) of the computing device’s CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), computing data structures, and the like, that perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, those of skill in the art will recognize that a computer program may be written in various versions of various languages. In some embodiments, APIs may comprise various languages, for example, languages in various releases of TensorFlow, Theano, Keras, PyTorch, or any combination thereof which may be implemented in various releases of Python, Python3, C, C#, C++, MatLab, R, Java, JAX, Julia, Rust, or any combination thereof.

[0248] The functionality of the computer readable instructions may be combined or distributed as desired in various environments. In some embodiments, a computer program comprises one sequence of instructions. In some embodiments, a computer program comprises a plurality of sequences of instructions. In some embodiments, a computer program is provided from one location. In other embodiments, a computer program is provided from a plurality of locations. In various embodiments, a computer program includes one or more software modules. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.- 72 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Web Application

[0249] In some embodiments, a computer program includes a web application. In some embodiments, a user may enter a query for generating synthetic data using a virtual environment, receiving synthetic data generated using a virtual environment, processing synthetic data to train a machine learning (ML) model, using a ML model to predict a plurality of attributes of a physical environment, determining one or more actions for a user to perform, predicting attributes of a real agent or a log or time history of a physical environment through a web application. In some embodiments, a user may generate synthetic data using a virtual environment, receive synthetic data generated using a virtual environment, process synthetic data to train a machine learning (ML) model, use a ML model to predict a plurality of attributes of a physical environment, determine one or more actions for a user to perform, predict attributes of a real agent or a log or time history of a physical environment through a web application. In some embodiments, a user may enter a query for measuring real data from a physical environment, process the real data measured from a physical environment or one or more organisms therein, process the real data using a ML model to predict a plurality of attributes of a physical environment or one or more organisms therein, or determining one or more actions for a user to perform through a web application. In some embodiments, a user may generate synthetic data using a virtual environment, receive synthetic data generated using a virtual environment, process synthetic data to train a machine learning (ML) model, use a ML model to predict a plurality of attributes of a physical environment, determine one or more actions for a user to perform, predict attributes of a real agent or a log or time history of a physical environment through a web application. In light of the disclosure provided herein, those of skill in the art will recognize that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems. In some embodiments, a web application is created upon a software framework such as Microsoft®. NET or Ruby on Rails (RoR). In some embodiments, a web application utilizes one or more database systems including, by way of non-limiting examples, relational, non-relational, object oriented, associative, XML, and document oriented database systems. In further embodiments, suitable relational database systems include, by way of non-limiting examples, Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the art will also recognize that a web application, in various embodiments, is written in one or more versions of one or more languages. A web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof. In some embodiments, a web- 73 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or extensible Markup Language (XML). In some embodiments, a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, a web application is written to some extent in a client-side scripting language such as Asynchronous JavaScript and XML (AJAX), Flash® ActionScript, JavaScript, or Silverlight®. In some embodiments, a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tel, Smalltalk, WebDNA®, or Groovy. In some embodiments, a web application is written to some extent in a database query language such as Structured Query Language (SQL). In some embodiments, a web application integrates enterprise server products such as IBM® Lotus Domino®.Mobile application

[0250] In some embodiments, a computer program includes a mobile application provided to a mobile computing device. In some embodiments, the mobile application is provided to a mobile computing device at the time it is manufactured. In other embodiments, the mobile application is provided to a mobile computing device via the computer network described herein.

[0251] In view of the disclosure provided herein, a mobile application is created by techniques known to those of skill in the art using hardware, languages, and development environments known to the art. Those of skill in the art will recognize that mobile applications are written in several languages. Suitable programming languages include, by way of non-limiting examples, C, C++, C#, Objective-C, Java™, JavaScript, Pascal, Object Pascal, Python™, Ruby, VB. NET, WML, and XHTML / HTML with or without CSS, or combinations thereof.

[0252] Suitable mobile application development environments are available from several sources. Commercially available development environments include, by way of non-limiting examples, AirplaySDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite,. NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are available without cost including, by way of non-limiting examples, Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile device manufacturers distribute software developer kits including, by way of non-limiting examples, iPhone and iPad (los) SDK, Android™ SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.- 74 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Standalone application

[0253] In some embodiments, a computer program includes a standalone application, which is a program that is run as an independent computer process, not an add-on to an existing process, e.g., not a plug-in. Those of skill in the art will recognize that standalone applications are often compiled. A compiler is a computer program(s) that transforms source code written in a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, by way of non-limiting examples, C, C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB. NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program. In some embodiments, a computer program includes one or more executable complied applications.Software Modules

[0254] In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and / or database modules, or use of the same. In view of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, a distributed computing resource, a cloud computing resource, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, a plurality of distributed computing resources, a plurality of cloud computing resources, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non-limiting examples, a web application, a mobile application, a standalone application, and a distributed or cloud computing application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on a distributed computing platform such as a cloud computing platform. In some embodiments, software modules are hosted on one or more machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location.- 75 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Databases

[0255] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for storage and retrieval of information about generating synthetic data using a virtual environment, receiving synthetic data generated using a virtual environment, processing synthetic data to train a machine learning (ML) model, using a ML model to predict a plurality of attributes of a physical environment, determining one or more actions for a user to perform, predicting attributes of a real agent or a log or time history of a physical environment, measuring real data from a physical environment, processing real data measured from a physical environment, processing the real data to predict a plurality of attributes of a physical environment or one or more organisms therein, or any combination thereof. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entity-relationship model databases, associative databases, XML databases, document oriented databases, and graph databases. Further non-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, Sybase, and MongoDB. In some embodiments, a database is Internet-based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing-based. In a particular embodiment, a database is a distributed database. In other embodiments, a database is based on one or more local computer storage devices.List of Embodiments

[0256] The following list of embodiments of the invention are to be considered as disclosing various features of the invention, which features can be considered to be specific to the particular embodiment under which they are discussed, or which are combinable with the various other features as listed in other embodiments. Thus, simply because a feature is discussed under one particular embodiment does not necessarily limit the use of that feature to that embodiment.

[0257] Embodiment 1. A computer-implemented method, comprising: (a) receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects; (b) processing the synthetic data to train a machine learning (ML) model to yield a trained ML model; (c) using the trained ML model to predict a plurality of attributes of a physical environment; and (d) determining, based at least on the predicting- 76 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601in (c), one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent.

[0258] Embodiment 2. The method of Embodiment 1, further comprising generating the synthetic data, wherein the synthetic data comprises a log, wherein the log comprises a plurality of attributes of each virtual sensor, virtual agent, or virtual object.

[0259] Embodiment 3. The method of Embodiment 2, further comprising using the log to automatically train the trained ML model, wherein the automatic training comprises supervised training or unsupervised training.

[0260] Embodiment 4. The method of Embodiment 3, wherein the physical environment comprises a plurality of (i) real sensors, (ii) real agents, or (iii) real objects.

[0261] Embodiment 5. The method of Embodiment 4, wherein the plurality of attributes comprises attributes associated with (i) each virtual sensor or real sensor, (ii) each virtual agent or real agent, or (iii) each virtual object or real object.

[0262] Embodiment 6. The method of Embodiment 4, wherein the trained ML model comprises a sensor-attribute model configured to (i) analyze data from the plurality of virtual sensors or real sensors and (ii) determine at least one attribute of the plurality of attributes.

[0263] Embodiment 7. The method of Embodiment 6, wherein generating the sensor-attribute model comprises: (a) receiving sensor data from (i) the plurality of virtual sensors, (ii) the plurality of real sensors, or both (i) and (ii); (b) processing the sensor data to determine attributes of the plurality of virtual agents or real agents; and (c) updating the synthetic data from the processing in (b) for use by the trained ML model based at least on the determined attributes.

[0264] Embodiment 8. The method of Embodiment 7, further comprising updating the sensorattribute model based at least on training the sensor-attribute model with the updated synthetic data.

[0265] Embodiment 9. The method of Embodiment 7, further comprising generating the synthetic data, wherein the synthetic data comprises a log, wherein the log comprises of a plurality of attribute data in the virtual environment.

[0266] Embodiment 10. The method of Embodiment 9, further comprising using the log to automatically train the sensor-attribute model, wherein the automatic training comprises supervised training or unsupervised training.- 77 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0267] Embodiment 11. The method of Embodiment 6, wherein the trained ML model further comprises an attribute-entity model configured to uniquely associate attributes to (i) each virtual agent, (ii) each real agent, or both (i) and (ii).

[0268] Embodiment 12. The method of Embodiment 11, wherein generating the attribute-entity model comprises: (a) receiving determined attributes from the sensor-attribute model and applying the determined attributes to (i) the virtual sensors, (ii) the real sensors, or both (i) and (ii); (b) processing the determined attributes to determine unique attributes for (i) each virtual agent, (ii) each real agent, or both (i) and (ii); and (c) updating the synthetic data from the processing in (b) for use by the trained ML model based at least on the determined unique attributes.

[0269] Embodiment 13. The method of Embodiment 12, further comprising updating the attribute-entity model based at least on training the attribute-entity model with the updated synthetic data.

[0270] Embodiment 14. The method of Embodiment 12, further comprising generating the synthetic data, wherein the synthetic data comprises a log, wherein the log comprises a plurality of entity data and a plurality of attribute data in the virtual environment.

[0271] Embodiment 15. The method of Embodiment 14, further comprising using the log to automatically train the attribute-entity model, wherein the automatic training comprises supervised training or unsupervised training.

[0272] Embodiment 16. The method of Embodiment 11, wherein the trained ML model further comprises an entity-attribute projection model configured to predict the plurality of attributes at one or more future time periods with a predetermined confidence level.

[0273] Embodiment 17. The method of Embodiment 16, wherein generating the entity-attribute projection model comprises: (a) receiving time-history data from (i) the virtual environment, (ii) the physical environment, or both (i) and (ii); (b) processing the time-history data to predict an attribute over time for (i) each virtual agent, (ii) each real agent, or both (i) and (ii); and (c) updating the synthetic data from the processing in (b) for use by the trained ML model based at least on actual attributes or the predicted attributes.

[0274] Embodiment 18. The method of Embodiment 17, further comprising updating the entityattribute projection model based at least on training the entity-attribute projection model with the updated synthetic data.- 78 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0275] Embodiment 19. The method of Embodiment 17, further comprising generating the synthetic data, wherein the synthetic data comprises a log, wherein the log comprises the time-history data in the virtual environment.

[0276] Embodiment 20. The method of Embodiment 19, further comprising using the log to automatically train the entity-attribute projection model, wherein the automatic training comprises supervised training or unsupervised training.

[0277] Embodiment 21. The method of Embodiment 16, wherein the trained ML model comprises each of (i) the sensor-attribute model, (ii) the attribute-entity model, and (iii) the entityattribute projection model.

[0278] Embodiment 22. The method of Embodiment 4, further comprising iteratively updating the trained ML model based at least on processing data from the plurality of virtual sensors or real sensors.

[0279] Embodiment 23. The method of Embodiment 4, further comprising using a stochastic model to generate attributes for each virtual agent, wherein the attributes comprise visual attributes, behavior attributes, or health attributes.

[0280] Embodiment 24. The method of Embodiment 4, wherein the virtual environment or the physical environment comprises an agricultural development facility, a production facility, or a processing facility for use with livestock, animals, aquaculture, row crops, greenhouse produce, greenhouse horticulture, or other agricultural sectors.

[0281] Embodiment 25. The method of Embodiment 4, wherein the plurality of attributes of the virtual environment or the physical environment comprises (i) environmental attributes of weather, temperature, humidity levels, precipitation, airflow, natural lighting, artificial lighting, soil characteristics, disease pressure, or bedding type, (ii) global attributes of agent density, agent uniformity, or a number of agents, or (iii) interactive attributes of feed type, feed delivery, feed availability, water delivery, water availability, or types of management practices, wherein the types of management practices comprise adjusting one or more parameters of chemical solutions, nutrient solutions, or physical solutions to alter at least one attribute of a real agent.

[0282] Embodiment 26. The method of Embodiment 4, wherein the plurality of attributes of each virtual sensor or real sensor comprises attributes associated with imaging detectors, radio frequency identification (RFID) detectors, motion detectors, pressure detectors, sound detectors, temperature- 79 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601detectors, humidity detectors, water detectors, chemical detectors, ammonia detectors, carbon dioxide detectors, hydrogen sulfide detectors, nitrogen detectors, or light detectors.

[0283] Embodiment 27. The method of Embodiment 4, wherein the plurality of virtual agents or real agents comprises animals, plants, fungi, protists, or bacteria.

[0284] Embodiment 28. The method of Embodiment 4, wherein the plurality of attributes of each virtual agent or real agent comprises attributes associated with weight, size, height, color, appearance, visual properties, activity level, feed conversion ratio, nutrient content, growth rate, growth stage, mortality risk, propensity for disease, health metric, propensity for a type of behavior, stress, or social behavior.

[0285] Embodiment 29. The method of Embodiment 4, wherein the plurality of attributes for each virtual object or real object comprises attributes associated with shelter structures, shelter enclosures, shelter sub-enclosures, bedding, soil, physical barriers, sensor calibration objects, waterers, feeders, irrigation systems, fans, feeders, light sources, sprayers, tractors, agricultural equipment, or physical implements.

[0286] Embodiment 30. The method of Embodiment 4, further comprising using the method to create a management plan based at least on the one or more actions.

[0287] Embodiment 31. The method of Embodiment 4, further comprising using the method for genetic selection of a virtual agent or a real agent determined to exhibit improved attributes over a baseline virtual agent or real agent.

[0288] Embodiment 32. The method of Embodiment 4, further comprising using the method to monitor health attributes in real time for each real agent.

[0289] Embodiment 33. The method of Embodiment 4, further comprising using the method to design, optimize, or improve a design of the physical environment over a baseline physical environment.

[0290] Embodiment 34. The method of Embodiment 4, wherein the one or more actions comprise adjusting (i) environmental parameters of the physical environment, (ii) feeding, watering, or other management parameters for the plurality of real agents, or (iii) density of the plurality of real agents.

[0291] Embodiment 35. The method of Embodiment 4, further comprising using a ML model to generate the virtual environment and the plurality of attributes of the virtual sensors, agents, and objects from data of the real sensors, agents, and objects received from the plurality of real sensors.- 80 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0292] Embodiment 36. The method of Embodiment 4, further comprising using actual data from the physical environment to iteratively update the trained ML model.

[0293] Embodiment 37. The method of Embodiment 4, further comprising prior to (a), developing the virtual environment to generate synthetic data, wherein an initial state of each virtual agent is determined by receiving observations of the plurality of real agents.

[0294] Embodiment 38. The method of Embodiment 4, further comprising displaying the one or more actions on a user interface (UI) configured with operable user tools which allow a user to select at least one action of the one or more of actions.

[0295] Embodiment 39. The method of Embodiment 4, wherein the synthetic data is associated with attributes of the plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects.

[0296] Embodiment 40. A system comprising at least one processor and instructions executable by the at least one processor to cause the at least one processor to perform operations comprising: (a) receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects; (b) processing the synthetic data to train a machine learning (ML) model to yield a trained ML model; (c) using the trained ML model to predict a plurality of attributes of a physical environment and a plurality of attributes of real agents in the physical environment; and (d) determining, based at least on the predicting in (c), one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent.

[0297] Embodiment 41. A computer- implemented method comprising processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories.

[0298] Embodiment 42. The computer-implemented method of Embodiment 41, wherein the processing comprises tracking each of the one or more identifiers for each of the one or more organisms.

[0299] Embodiment 43. The computer-implemented method of Embodiment 41 or 42, further comprising obtaining one or more biological or behavioral features throughout the one or more trajectories.

[0300] Embodiment 44. The computer-implemented method of Embodiment 43, further comprising assigning the one or more biological or behavioral features to the one or more organisms.- 81 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0301] Embodiment 45. The computer-implemented method of Embodiment 44, further comprising assigning one or more phenotypes to the one or more organisms based on the one or more biological or behavioral features.

[0302] Embodiment 46. The computer-implemented method of Embodiment 45, wherein a subset of the one or more phenotypes are not available for a subset of the one or more organisms.

[0303] Embodiment 47. The computer-implemented method of Embodiment 46, further comprising generating estimates of the subset of the one or more phenotypes for the subset of the one or more organisms.

[0304] Embodiment 48. The computer- implemented method of any one of Embodiments 41-47, further comprising combining one or more features along the one or more trajectories to build one or more models for estimating one or more unavailable features along that trajectory, wherein the one or more features comprise the one or more identifiers, the one or more biological or behavioral features, the one or more phenotypes, or any combination thereof.

[0305] Embodiment 49. The computer- implemented method of any one of Embodiments 41-48, wherein the one or more identifiers comprise one identifier.

[0306] Embodiment 50. The computer-implemented method of any one of Embodiments 41-49, wherein the one or more biological or behavioral features comprise one biological or behavioral feature.

[0307] Embodiment 51. The computer- implemented method of any one of Embodiments 41-50, wherein the one or more phenotypes comprise one phenotype.

[0308] Embodiment 52. The computer- implemented method of any one of Embodiments 41-51, wherein the one or more trajectories comprise a plurality of trajectories.

[0309] Embodiment 53. The computer-implemented method of any one of Embodiments 41-52, wherein the one or more organisms comprise a plurality of organisms.

[0310] Embodiment 54. The computer- implemented method of any one of Embodiments 41-53, wherein the one or more identifiers comprise a plurality of identifiers.

[0311] Embodiment 55. The computer-implemented method of any one of Embodiments 41-54, wherein the one or more biological or behavioral features comprise a plurality of biological or behavioral features.

[0312] Embodiment 56. The computer-implemented method of any one of Embodiments 41-55, wherein the one or more phenotypes comprise a plurality of phenotypes.- 82 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0313] Embodiment 57. The computer-implemented method of any one of Embodiments 41-56, wherein each of the one or more trajectories comprise a discrete trajectory of a discrete organism of the one or more organisms.

[0314] Embodiment 58. The computer- implemented method of any one of Embodiments 41-57, wherein the one or more organisms comprise one or more livestock animals or one or more plants.

[0315] Embodiment 59. The computer-implemented method of any one of Embodiments 41-58, wherein the one or more livestock animals comprise one or more birds, swine, cattle, goats, sheep, or any combination thereof.

[0316] Embodiment 60. The computer- implemented method of any one of Embodiments 41-59, further comprising obtaining the one or more trajectories.

[0317] Embodiment 61. The computer-implemented method of Embodiment 60, wherein the obtaining the one or more trajectories comprises receiving, generating, or measuring the one or more trajectories.

[0318] Embodiment 62. The computer-implemented method of Embodiment 61, wherein the measuring is performed using vision tracking, visual identifiers, ultra-high or high frequency RFID, lidar, or any combination thereof.

[0319] Embodiment 63. The computer-implemented method of Embodiment 61 or 62, wherein the measuring is performed using a plurality of cameras.

[0320] Embodiment 64. The computer- implemented method of any one of Embodiments 61-63, wherein the measuring is performed using image segmentation based on the plurality of cameras.

[0321] Embodiment 65. The computer-implemented method of any one of Embodiments 41-64, wherein the one or more trajectories comprise one or more 2D or 3D trajectories.

[0322] Embodiment 66. The computer-implemented method of any one of Embodiments 41-65, wherein the one or more trajectories are measured from an environment comprising one or more obstructions or obstacles.

[0323] Embodiment 67. The computer-implemented method of Embodiment 66, wherein the one or more obstructions or obstacles comprise: a food source, a water source, a shelter, or any combination thereof.

[0324] Embodiment 68. The computer- implemented method of any one of Embodiments 43-67, wherein the obtaining the one or more biological or behavioral features comprises receiving, generating, or measuring the one or more biological or behavioral features.- 83 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0325] Embodiment 69. The computer-implemented method of Embodiment 68, wherein the measuring is performed using a sensor, a human intervention, a model, or any combination thereof.

[0326] Embodiment 70. The computer- implemented method of Embodiment 69, wherein the model is a vision to weight model.

[0327] Embodiment 71. The computer-implemented method of any one of Embodiments 43-70, wherein the one or more biological or behavioral features comprise: a feeding event, a drinking event, a urinating event, a defecating event, a mating event, a reproducing event, a production event, an injury event, an illness event, a death event, an aggression event, a socializing event, a weight, a height, a movement pattern, a visual, a diagnosis, a prognosis, a symptom, an epidermal feature, or any combination thereof.

[0328] Embodiment 72. The computer-implemented method of Embodiment 70, wherein the epidermal feature comprises a visual appearance or optical feature of feather, fur, hair, beak, eyes, skin, or any combination thereof.

[0329] Embodiment 73. The computer- implemented method of any one of Embodiments 41-72, further comprising obtaining the one or more identifiers.

[0330] Embodiment 74. The computer- implemented method of Embodiment 73, wherein the obtaining the one or more identifiers comprises receiving, generating, or measuring the one or more identifiers.

[0331] Embodiment 75. The computer-implemented method of Embodiment 73 or 74, wherein the measuring is performed by detecting a visual identifier, a tag, a barcode, RFID, human annotation, or any combination thereof.

[0332] Embodiment 76. The computer- implemented method of any one of Embodiments 45-75, wherein the assigning one or more phenotypes is performed using a model.

[0333] Embodiment 77. The computer-implemented method of Embodiment 76, wherein the model is a deterministic model the processes (i) annotated phenotype data of a first subset of the one or more organisms and (ii) sensor data of a second subset of the one or more organisms, to determine the one or more phenotypes for the second subset of the one or more organisms.

[0334] Embodiment 78. The computer-implemented method of Embodiment 76 or 77, wherein the model is a machine learning model trained to process one or more images to determine the one or more phenotypes.- 84 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0335] Embodiment 79. The computer- implemented method of any one of Embodiments 45-78, wherein the one or more phenotypes comprise: a health state, an injury state, a disease state, a feather score, a health score, a death state, a disease score, an activity score, feeding time, drinking time, number of eggs laid, time spent in a nest box, total distance traveled, weight, time in scratch area, time on slats, confidence score of any of the preceding, or any combination thereof.

[0336] Embodiment 80. The computer- implemented method of any one of Embodiments 41-79, further comprising isolating an organism from the one or more organisms based on a phenotype of the organism.

[0337] Embodiment 81. The computer- implemented method of Embodiment 80, wherein the phenotype of the organism comprises an infectious disease state, lameness state, or any other unhealthy, non-productive, or risk inducing state.

[0338] Embodiment 82. A computer-implemented method of training a neural network, comprising: (a) collecting (i) one or more trajectories of one or more organisms and (ii) one or more biological or behavioral features of one or more organisms throughout the one or more trajectories; (b) processing the (i) one or more trajectories of one or more organisms and (ii) the one or more biological or behavioral features of one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms; and (c) training the machine learning model to reduce a loss function that quantifies an error associated with the one or more predictions of the one or more phenotypes compared to one or more ground-truth values of the one or more phenotypes.

[0339] Embodiment 83. The computer- implemented method of Embodiment 82, wherein the one or more trajectories, the one or more biological or behavioral features, the one or more groundtruth values, or any combination thereof, comprises real data, synthetic data, or both.

[0340] Embodiment 84. A computer-implemented method of predicting one or more phenotypes of one or more organisms, comprising: (a) processing (i) one or more trajectories of the one or more organisms and (ii) one or more biological or behavioral features of the one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms.

[0341] Embodiment 85. The computer-implemented method of Embodiment 84, further comprising processing (iii) a second set of one or more phenotypes of a second set of one or more- 85 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601organisms to generate the one or more predictions of the one or more phenotypes of the one or more organisms.

[0342] Embodiment 86. A system comprising one or more processors independently or collectively configured for processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories.

[0343] Embodiment 87. The system of Embodiment 86, wherein the processing comprises tracking each of the one or more identifiers for each of the one or more organisms.

[0344] Embodiment 88. The system of Embodiment 86 or 87, further configured for obtaining one or more biological or behavioral features throughout the one or more trajectories.

[0345] Embodiment 89. The system of Embodiment 88, further configured for assigning the one or more biological or behavioral features to the one or more organisms.

[0346] Embodiment 90. The system of Embodiment 89, further configured for assigning one or more phenotypes to the one or more organisms based on the one or more biological or behavioral features.

[0347] Embodiment 91. The system of Embodiment 90, wherein a subset of the one or more phenotypes are not available for a subset of the one or more organisms.

[0348] Embodiment 92. The system of Embodiment 91, further configured for generating estimates of the subset of the one or more phenotypes for the subset of the one or more organisms.

[0349] Embodiment 93. The system of any one of Embodiments 86-92, further configured for combining one or more features along the one or more trajectories to build one or more models for estimating one or more unavailable features along that trajectory, wherein the one or more features comprise the one or more identifiers, the one or more biological or behavioral features, the one or more phenotypes, or any combination thereof.

[0350] Embodiment 94. The system of any one of Embodiments 86-93, wherein the one or more organisms comprise one or more livestock animals.

[0351] Embodiment 95. The system of any one of Embodiments 86-94, wherein the one or more livestock animals comprise one or more birds.

[0352] Embodiment 96. The system of any one of Embodiments 86-95, further configured for obtaining the one or more trajectories.

[0353] Embodiment 97. The system of Embodiment 96, wherein the obtaining the one or more trajectories comprises receiving, generating, or measuring the one or more trajectories.- 86 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0354] Embodiment 98. The system of Embodiment 97, wherein the measuring is performed using vision tracking, visual identifiers, ultra-high or high frequency RFID, a lidar, or any combination thereof.

[0355] Embodiment 99. The system of Embodiment 97 or 98, wherein the measuring is performed using a plurality of cameras.

[0356] Embodiment 100. The system of any one of Embodiments 97-99, wherein the measuring is performed using image segmentation based on the plurality of cameras.

[0357] Embodiment 101. The system of any one of Embodiments 86-100, wherein the one or more trajectories comprise one or more 2D or 3D trajectories.

[0358] Embodiment 102. The system of any one of Embodiments 86-101, wherein the one or more trajectories are measured from an environment comprising one or more obstructions or obstacles.

[0359] Embodiment 103. The system of Embodiment 102, wherein the one or more obstructions or obstacles comprise: a food source, a water source, a shelter, or any combination thereof.

[0360] Embodiment 104. The system of any one of Embodiments 88-103, wherein the obtaining the one or more biological or behavioral features comprises receiving, generating, or measuring the one or more biological or behavioral features.

[0361] Embodiment 105. The system of Embodiment 104, wherein the measuring is performed using a sensor, a human intervention, a model, or any combination thereof.

[0362] Embodiment 106. The system of Embodiment 105, wherein the model is a vision to weight model.

[0363] Embodiment 107. The system of any one of Embodiments 88-106, wherein the one or more biological or behavioral features comprise: a feeding event, a drinking event, a urinating event, a defecating event, a mating event, a reproducing event, a production event, an injury event, an illness event, a death event, an aggression event, a socializing event, a weight, a height, a movement pattern, a visual, a diagnosis, a prognosis, a symptom, an epidermal feature, or any combination thereof.

[0364] Embodiment 108. The system of Embodiment 107, wherein the epidermal feature comprises a visual appearance or optical feature of feather, fur, hair, beak, eyes, skin, or any combination thereof.

[0365] Embodiment 109. The system of any one of Embodiments 86-108, further configured for obtaining the one or more identifiers.- 87 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0366] Embodiment 110. The system of Embodiment 109, wherein the obtaining the one or more identifiers comprises receiving, generating, or measuring the one or more identifiers.

[0367] Embodiment 111. The system of Embodiment 109 or 110, wherein the measuring is performed by detecting a visual identifier, a tag, a barcode, RFID, human annotation, or any combination thereof.

[0368] Embodiment 112. The system of any one of Embodiments 90-111, wherein the assigning one or more phenotypes is performed using a model.

[0369] Embodiment 113. The system of Embodiment 112, wherein the model is a deterministic model the processes (i) annotated phenotype data of a first subset of the one or more organisms and (ii) sensor data of a second subset of the one or more organisms, to determine the one or more phenotypes for the second subset of the one or more organisms.

[0370] Embodiment 114. The system of Embodiment 112 or 113, wherein the model is a machine learning model trained to process one or more images to determine the one or more phenotypes.

[0371] Embodiment 115. The system of any one of Embodiments 90-114, wherein the one or more phenotypes comprise: a health state, an injury state, a disease state, a feather score, a health score, a death state, a disease score, an activity score, feeding time, drinking time, number of eggs laid, time spent in a nest box, total distance traveled, weight, time in scratch area, time on slats, confidence score of any of the preceding, or any combination thereof.

[0372] Embodiment 116. The system of any one of Embodiments 86-115, further configured for isolating an organism from the one or more organisms based on a phenotype of the organism.

[0373] Embodiment 117. The system of Embodiment 116, wherein the phenotype of the organism comprises an infectious disease state, lameness state, or any other unhealthy, non-productive, or risk inducing state.

[0374] Embodiment 118. A system comprising one or more processors independently or collectively configured for training a machine learning model by: (a) collecting (i) one or more trajectories of one or more organisms and (ii) one or more biological or behavioral features of one or more organisms throughout the one or more trajectories; (b) processing (i) the one or more trajectories of one or more organisms and (ii) the one or more biological or behavioral features of one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms; and (c) training the machine learning model to reduce- 88 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601a loss function that quantifies an error associated with the one or more predictions of the one or more phenotypes compared to one or more ground-truth values of the one or more phenotypes.

[0375] Embodiment 119. The system of Embodiment 118, wherein the one or more trajectories, the one or more biological or behavioral features, the one or more ground-truth values, or any combination thereof, comprises real data, synthetic data, or both.

[0376] Embodiment 120. A system comprising one or more processors independently or collectively configured for predicting one or more phenotypes of one or more organisms by: (a) processing (i) one or more trajectories of the one or more organisms and (ii) one or more biological or behavioral features of the one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms.

[0377] Embodiment 121. The system of Embodiment 120, further configured for processing (iii) a second set of one or more phenotypes of a second set of one or more organisms to generate the one or more predictions of the one or more phenotypes of the one or more organisms.

[0378] Embodiment 122. A computer program product comprising computer-executable instructions for: (a) receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects; (b) processing the synthetic data to train a machine learning (ML) model to yield a trained ML model; (c) using the trained ML model to predict a plurality of attributes of a physical environment; and (d) determining, based at least on the predicting in (c), one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent.

[0379] Embodiment 123. A computer program product comprising computer-executable instructions for processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories.

[0380] Embodiment 124. A system comprising a digital twin of an agricultural environment comprising a plurality of virtual agents that tracks a plurality of real entities at an entity-level resolution.EXAMPLES

[0381] The following examples are provided to further illustrate some embodiments of the present disclosure, but are not intended to limit the scope of the disclosure; it will be understood by their- 89 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601exemplary nature that other procedures, methodologies, or techniques known to those skilled in the art may alternatively be used.Example 1: Tracking Algorithm

[0382] This example provides an AI platform for modeling and predicting features of physical agricultural environments. FIG. 18 shows an example high-level schematic of the AI platform. A VAE is built to replicate the physical space and the conditions of a physical environment. The models of the Al platform are trained with synthetic data generated from the VAE. Images of a population of virtual agents are gathered through sequential data acquisition, which couples a tracking model and the virtual environment with a rare event active learning strategy.

[0383] The VAE is used to run thousands of simulations that simultaneously train a facility-specific AI tracking model and optimize sensor placement. This increases tracking accuracy while reducing sensor costs. The facility is then equipped with the virtual facility sensor configuration and the accompanying model is implemented to provide real-time insights and phenotypic data. Each iteration refines the logic the virtual agents to align with new insights from each customer’s facility.

[0384] The tracking model is able to identify and track 10 virtual chickens for 1 day in a coop with dimensions 5 meters (m) × 5 m in synthetic data generated using a simple virtual facility sampled at 4-5 frames per second. FIG. 19 shows an example approach for training models (Left) and tracking performance (Right) of 10 simulations of 10 chickens over 1 day in a VAE using the Al platform versus comparative methods. Data from a virtual poultry environment comprises automatically annotated images and known bird trajectories, which are used to train an AI chicken identifier model, and an AI chicken tracker model. The performance of 10 simulations with 10 chickens over 1 day in the VAE using the model is compared to YOLOv8™ + SORT™ in the plot on the right. Solid lines indicate the mean performance between all 10 simulations, and dashed lines indicate the minimum and maximum performances.

[0385] The solid lines show the average of 10 experimental trials with simulated data, with the dashed line the minimum and maximum trials for each method.

[0386] Comparative methods only maintain unique IDs of chickens on the order of minutes, with all 10 chickens lost at the 10-minute mark and in every trial. By contrast, even after a full day, the tracking model of the present application tracked 90% of chickens on average over the 10 trials. The improvement over other methods can be attributed to, in part, the chicken identifier, the chicken tracker, and the active learning training algorithm.- 90 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0387] Chicken Identifier: While the results shown use YOLOv8 for object identification in both cases, the key difference is in the way these models are trained. A typical implementation requires annotation of many poultry images to train and validate the identification algorithm. There is a double advantage to generating synthetic data. Not only can systems and methods herein generate unlimited data to train on, but the data can also be annotated automatically, given that the true position of all chickens is known in our simulations, and thus bounding boxes for each object can be generated without the need for manual annotation. Additionally, this approach can embed visual noise, in terms of lighting, smudging, etc. that the identifier learns to overcome.

[0388] Chicken Tracker: The chicken tracker is a generative model that leverages a transformer architecture and is trained to resolve issues that readily appear with methods such as SORT. These issues are accumulating tracking errors, lost chickens, and misidentified chickens.

[0389] The tracking model uses various sources of information as input, and is trained on known ground truth as to the location of each chicken to track individual trajectories. The SORT algorithm, commonly used for tracking, uses a constant velocity assumption between frames paired with a Kalman filter to track objects. Instead, the transformer based tracking model takes as input the previous trajectory of a chicken, its unique statistics, position, and SORT estimation to track objects with improved accuracy. The tracking model is not only less likely to ‘lose’ a chicken, but it has the capability to automatically reassign lost IDs as it leams typical trajectories of different chickens (e.g. an active versus an inactive bird). SORT estimates are currently used as one of the inputs to the tracking model, but this can readily be complemented or replaced by estimates from other tracking methods, such as Particle Image Velocimetry (PIV) or Particle Tracking Velocimetry (PTV).

[0390] FIG. 20 shows an example schematic of a generative model having a transformer architecture, in accordance with some embodiments. The generative model solves three common issues experienced by deterministic models, such as Simple Online and Realtime Tracking (SORT): accumulated loss of position, complete loss of chicken position, and misidentification. The model’s input are tokens of chicken ID, past positions, and the SORT current position, which it then uses to predict chicken ID and position

[0391] Generative Al techniques can be incredibly powerful and flexible; however, they often require massive amounts of data to initially train. The Al platform provides the massive amounts of synthetic data generated via the VAE. While the use of simulations to generate data means that synthetic data can be generated as necessary to train the models, the amount of training requirements are also reduced- 91 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601by employing a rare event active learning strategy during training. These methods enable much faster exploration of the sample space by actively training on the most ‘useful’ data, representing edge cases and rare events that the model can learn from the most. This keeps the number of simulations required low and enables fast training on available data.

[0392] Training Algorithms & Sensor Placement

[0393] Although the virtual facility provides an identical setup to that of a physical environment, the transition to the physical world can involve practical considerations as to the installation, set up, and maintenance of hardware, as well as ensuring the data quality. Unforeseen gaps between Al and physical environment can be expected. Optimum sensor placement is designed by starting with a large number of redundant sensors in the VAE that are progressively reduced following Bayesian optimization and rare and extreme optimal sensing principles.

[0394] Tracking poultry in a physical poultry house is likely to produce outcomes unforeseen in the virtual facilities. Generating new synthetic data replicating these unforeseen edge cases, re-training, and iterating on real gathered data with the model directly, can improve performance of the Al platform over time. This approach ensures VAE accuracy even for edge cases. Active learning algorithms designed explicitly for identifying rare edge cases to build “rare and extreme event” Al can be used, where the Al iteratively and dynamically queries experiments or simulations to improve its performance. Applying these methods can allow efficient and swift use of VPE simulations by actively identifying and training on the most ‘informative’ edge case data.Example 2: Modeling a Physical Environment with a Virtual Environment

[0395] This example describes determining (e.g., scanning) and reconstructing a commercial broiler house to apply the Al platform to environments with a high-density of bird flocks. FIG.21 shows an example high-level schematic of the Al platform. The approach can involve replicating a physical environment with a virtual environment, training models based on synthetic data from the virtual environment, and applying models to make predictions on the physical environment. Leveraging a commercial broiler house virtual facility, various scenarios that reflect the real-life conditions of commercial operations can be simulated and used to train the Al platform for deployment and use in real, commercial facilities to provide actionable insights.

[0396] In examples considering smaller breeding farms, the Al platform can carefully track individualized bird health phenotypes - including activity levels, physical abnormalities, disposition to disease, and other observable behaviors - to enable vast improvements to genetic selection- 92 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601programs. In commercial farms, and breeding farms, health monitoring is also needed to prioritize management practices more effectively. Automatic detection of unusual behavior, disease onset, or dead birds can far enhance the productivity of poultry operations and the throughput of their flock supervisors. This can directly improve bird livability and provide a framework to better understand the impact of environmental variables and management practices on bird health, enhancing performance and yield.

[0397] A layout of a VAE is generated by scanning a broiler house with modern mapping technology, such as point clouds, neural radiance fields, or Gaussian splatting. The layout of the broiler house is used to reconstruct an accurate representation of the physical environment in the VAE. The VAE is populated with the appropriate arrangement and density of virtual chickens, objects, and sensors. Sensor and object placement is optimized in the VAE, and then sensors and objects are placed in the corresponding locations in the broiler house. The Al platform provides high fidelity poultry tracking and phenotyping for the broiler house using the continuously improving models and VAE.

[0398] The virtual chickens are modeled using a set of internal logic, unique to each bird so each can have slightly different behaviors. The virtual chicken eats, sleeps, drinks, interacts with other birds, makes friends and gets stressed, can contract diseases or get injured. Drawing from poultry behavior literature, virtual chicken agent embeds logic sufficient so that the virtual flock replicates, statistically, the dynamics of real flocks. Specifically, a breed-specific Al platform can be created. FIG. 22 shows an example feedback loop for dynamic and continuous iteration of the Al platform. By adapting the virtual agents’ logic to match the observed behaviors of real individual chickens as well as the flock, the VAE can be tailored to different breeds of chickens.

[0399] The VAE can continuously generate synthetic data. Models of the Al platform can be continuously trained on new synthetic data with different parameters, setups and sensor locations. Known “ground truth” values as to chicken position, gait score, activity level, etc. simplifies and automates training of the models. Manual data annotation may not be necessary, because the full true trajectories of each individual chicken is already known from the VAE. Given full control over the simulation parameters, algorithms identify and train on particular scenarios that pose difficulty to the model or provide crucial information. The models are trained on various edge cases, including obstructions, unusual chicken behavior, and wall, flooring or lighting conditions which make bird tracking more challenging.- 93 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0400] The models are trained to identify and track chickens on thousands of simulations run in the VAE. Using the simulations, an efficient arrangement of sensors to achieve 100% poultry tracking can be determined. This simultaneous approach to model training and sensor placement optimization, constrained by known possible sensor locations, can reduce the cost and improve feasibility of tracking for the facility.

[0401] Health metrics such as body weight, gait score, feeding and drinking behavior of real birds are monitored using a tracking model. The tracking model is an Al resolver that takes as inputs an ensemble of estimates from various tracking algorithms, as well as dynamics of the virtual agents, and statistics and phenotypes of each chicken ID trajectory. Based on all this information, the resolver outputs the coordinates and associated states for each chicken ID. This resolver is trained to reestablish “lost chickens” given these various inputs.

[0402] The models are trained using rare event active learning (R.E.A.L.) algorithms to efficiently train the precision poultry tracking model. The method focuses training on the most informative segments of the simulation space, optimizing learning efficiency and effectiveness. To ensure effective model training, simulations are designed to be at least as challenging, if not more so, than actual field conditions. The inclusion of numerous obstacles and edge cases in the simulation environment pushes the Al to ensure robust capabilities that can handle any real-world scenario. This approach not only enhances the model’s performance but also significantly reduces the risk and cost associated with training.

[0403] In the approach to optimizing poultry health monitoring systems, the sensor placement and model tracking algorithm are developed in tandem to ensure maximum efficiency and accuracy. As the model is trained using simulation data from the virtual facility, the algorithm concurrently refines the placement of sensors to optimize data capture. This placement is constrained by realistic anchor points within the facility, ensuring that the sensor setup is both practical and effective. Initially, a large number of sensors are deployed to cover all possible areas of interest; however, as the model’s tracking accuracy is verified, the number of sensors are progressively reduced. By balancing the number of sensors against the Al’s tracking capabilities, full operational coverage with minimal hardware is ensured, optimizing both cost and performance in real-world scenarios. Alternatively, the use of additional sensors such as wearable devices or microphones may be considered to complement imaging and improve tracking accuracy.- 94 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0404] Health phenotypes are tracked over each bird’s lifespan, including activity indices such as total distance and gait scores to assess mobility and leg health. Behavioral patterns such as position, feeding, drinking, or piling can indicate stress levels, social interactions, and potential health issues. Sudden activity drops can warn of disease, enabling timely interventions to reduce the spread of illness. Continuous monitoring provides a comprehensive understanding of each bird’s health and informs decisions to enhance welfare and productivity.

[0405] The Al platform includes an image-derived health phenotyping model. The model considers behaviors like pecking, bird interactions, better understanding of drinking and eating patterns, and early disease indicators. Advanced imaging techniques allows detection of health issues precisely. The phenotypes identifies individual health problems and improve overall flock management, refining the tracking system for comprehensive poultry health management.

[0406] The Al platform can be applied to a broiler facility. Sensors and objects can be installed in the broiler facility based on the model-determined hardware setup. The capability of the model to maintain individualized chicken tracking can be tested through the lifespan of a real-world flock. Health metrics can be monitored using the real-time virtual representation.

[0407] The hardware setup includes high-resolution cameras strategically placed throughout the facility to ensure comprehensive coverage and accurate tracking of individual birds. The cameras are connected to local GPU-powered computing units, which process the video feeds in real-time to identify and monitor each bird’s health metrics.

[0408] Computing can be split between local hardware and cloud-based computing. To avoid handling excessive data, a local computing infrastructure can process camera images to identify individual chickens, outputting coordinates and state variables for each identified chicken on each camera. Considering only 40 cameras, data can exceed 100 MB / s. Storing or transferring this volume can be very expensive. Instead of reducing resolution or data rate, the data can be preprocessed onsite. Cameras can feed into an onsite GPU-powered computer for object identification, returning coordinates and states of identified chickens. These more concise data sets can be stored and transferred to the cloud for real-time analysis, keeping local costs low while performing computeintensive tasks upstream. If an anomaly or management action trigger is detected, a signal is sent to the facility, which can begin saving annotated images at 1 frame per second for manual review. New analog compute systems for Al can further reduce local hardware needs and costs.- 95 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0409] Hardware malfunctions can disrupt operations, so a maintenance and monitoring schedule can be implemented and backup components are available for replacement. Large data storage requirements, despite the split computing setup, can be addressed by using scalable cloud storage solutions and efficient data compression. Optimized algorithms and distributed computing can mitigate high computational demands, whether onsite or cloud- based. To combat unstable connections, local data buffering can be implemented to store data temporarily until connectivity is restored, and use redundant network paths and reliable internet service providers to enhance stability.

[0410] Active learning can be used to continuously improve the Al platform. Metrics to compare virtual agents to real chickens include distance covered per day / lifetime, number of hours sleeping / sitting, average gait score, propensity to get diseased and slow down, feed consumption at each stage of life, propensity to gather in groups, etc. Each iteration can update the internal logic of the agents, and in time it can behave indistinguishably from a real chicken and achieve statistical equivalency with real flocks. Similarly, the Al tracking resolver can learn and improve with each experiment. Each iteration can thus use an improved model, trained on more realistic synthetic data, enhancing performance with each iteration.

[0411] FIG. 23 shows an example graphical representation of quantitative goals of implementing the Al platform. The performance of the Al platform can be measured by the percentage of unique poultry IDs maintained over 30 days, and the cost can be measured by the number of cameras / sensors. One set of objectives are to demonstrate Al performance in the virtual facility, the ability to reduce cameras via optimal placement, and Al improvements through iterative active learning techniques. Another set of objectives are to demonstrate real- world performance, measuring unique IDs maintained over the lifespan of a flock.Example 3: Tracking and Health Monitoring in Real Facilities

[0412] Systems and methods herein were used to demonstrate per-bird Al Tracking and Health Monitoring. This example also describes prophetically running a 1,500-bird trial of per-bird Al Tracking and Health Monitoring tool for poultry in a commercial setting. The project entails collecting over 30 billion position data points and capture 150+ welfare events — yielding the largest tracking dataset to date. This data can test the system in its various capabilities: building centimeter-level trajectories, linking months of trajectories and behavior to health phenotypes with RFID, and testing the feasibility and resilience of the system.- 96 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0413] The system performs per-bird, high-resolution phenotyping and health-monitoring by combining a poultry-specific Kalman-filter tracker, Bayesian-optimization active learning, a Virtual Agriculture Environment (VAE) for synthetic data generation, and a customized hardware system built for commercial scaling. The tracker has been shown to outperform other tracking technologies by tracking 10x longer and monitoring 2–100x more birds simultaneously, and the preliminary trials have explicitly linked low-activity trajectories to lameness and mortality (flagging 3 dead and 3 lamed birds with tracking). The active learning approach permits the system’s model(s) to be tuned, including 10s of parameters in the tracker, to achieve unprecedented poultry tracking results, as well as quickly (e.g., one-day) fine-tuning computer vision model(s) in new environments. The VAE also leads to even greater fine-tuning of the models. Together, these advancements can provide the resolution for precision poultry and livestock.

[0414] The World Organization of Animal Health has estimated, in 2016, that $300B is lost globally due to poor animal health and automating per-animal monitoring can reduce this impact. In poultry and the US alone, automating monitoring can reduce poultry mortality, cut $4B in annual losses, and lower 6 MMT of CO2emissions, improving industry ESG and food security. A digital-twin of a farm can be created, and a digital-twin copilot can empower breeders, growers, insurers, and regulators with actionable insights, accelerating genetic gains and welfare interventions. Successful commercialization can catalyze wider adoption across livestock and preclinical animal trials, strengthening supply-chain resilience and addressing critical labor shortages in food production.

[0415] With global food demand set to climb 70% in the next 25 years, it is not merely genomics but a lack of high-resolution phenotyping that is presenting one of the greatest challenges in Ag R& D and production for engineering better products and preventing producers from fine-tuning management. As shown in FIG. 24, the system can: 1) serve as the premier high-fidelity phenotyping platform for animal breeding and health companies; and 2) leverage data to power digital-twin copilots for commercial producers, governmental supply-chain monitors, and livestock insurers. Poultry, where limited health phenotyping has led to 6% mortality (up from 3% in 2014 National Chicken Council (2023)) and $24 billion in global losses and 64 million metric tons of wasted CO2is in particular need of high-resolution health phenotypes. Breeders and animal-health firms constitute an important practical application for the platform, harnessing high-resolution phenotypes to accelerate genetic gains and welfare assessments. However, the resolution of data collection is critical to power future- 97 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601digital twins that will optimize commercial production, monitor supply chains, and lower insurance premiums in poultry and beyond.

[0416] The system can comprise a precision poultry tracker that produces 10x longer trajectories on 2-100x more birds than other tracking technologies. Tracking at this precision can lead to connecting the dots between a bird and its phenotypes exhibited throughout the facility, as shown in FIG. 25.Beyond clear applications in breeding / health companies for assessing bird genetics or impact of therapies, these trajectories unlock behavioral causality that can be leveraged to optimize commercial operations. While the construction of the tracker, and the association of observations along the trajectory, is unique in itself, the technology stack is built to further optimize the tracking capabilities with 1) active learning to accelerate model fine-tuning across disparate environments and facilities; 2) a synthetic-data engine that slashes manual annotation, continuously improving performance and resolving edge cases; and 3) topology-aware optimizations dynamically balance compute loads across large operations. Together, these innovations can permit a precise, individual-level phenotyping log at scale to unlock true precision agriculture and create the foundational data for tomorrow’s agricultural digital twins.

[0417] The system can be implemented as a Software+Hardware as a Service (SHaaS). The SHaaS implementation can provide high-fidelity Al phenotyping and health monitoring platform & UI, as shown in FIG. 26. The dashboard can provide an appealing interface for both growers / supervisors and breeding / health companies. The “Flock Dashboard” provides high-level statistics and alerts (e.g. down birds or stress events) for the entire flock (no ID association), while the “Bird Phenotypic Log” couples tracking and RFID, but not limited to only RFID for identification e.g. visual identifiers, to record bird behaviors and activities to enable genomic selection, health trials, and management optimization.

[0418] The system can be implemented with four different elements, shown in FIG. 27. The technology can comprise a precision poultry tracker, which can be a poultry-specific Kalman Filter tracking algorithm. Additional elements of the system, in conjunction with the tracker, can provide unmatched accuracy.Demonstrating the Bird-Resolution Tracking Difference

[0419] FIG. 28 shows images from progressive trials using side- and overhead-mounted camera systems demonstrate scalable activity tracking across broiler and breeder environments. From early proof-of-concept (Sasso broilers) to 24 / 7 management experiments and 100-bird livability trials, these- 98 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601deployments validated individual-level monitoring — including under low-light conditions using night vision.How Tracking Differentiates: Pinpointing Health Critical Birds & Linking Activity to Mgmt.

[0420] This section demonstrates the technology’s ability to 1) differentiate activity between management practices and pinpoint exact, health critical birds with tracking and 2) build bird-specific health logs, associating hours of behaviors per day, to inform breeding decisions, animal health therapies / drugs, new management products (e.g. ventilation) or other R& D / productions questions associated with bird-resolution.

[0421] FIG.29 shows that the tracking technology is able to 1) pinpoint health critical birds and their locations and 2) differentiate activity between flocks of varied management (limited vs. unlimited feed). This experiment split a bird pen into two 150 sq. ft. sections of 50 birds each, varying diet across management across each group. Utilizing an automated weighing vision model and the tracker, clear links between tracking activity and weight / management were observed. Zero broilers on the high activity side died prematurely or had broken legs, compared to the 3 deaths and 3 lame (broken legs) birds at processing time. FIG. 29 clearly shows 1 dead bird (2 previously removed) and 3 lame birds as clear tracking outliers, i.e. long-time trajectories with little movement. Utilizing the activity scores of these birds, this data informs growers that harvesting birds as their health become critical would have reduced losses.Providing Breeders with Bird-Resolution Phenotypes: Broiler Breeder Health Phenotyping Study (30-Week Breeder)

[0422] One-month into data collection and model fine-tuning, the tracker+ID (RFID in this embodiment) system was able to associate 204 daytime minutes of bird phenotyping on average per bird per day (16 hours / day). This means that the technology can provide over 700 hours of daytime phenotyping to breeding / health companies at current model tuning. With further system fine-tuning (model, sensors, etc.), this coverage can be tripled, approaching the daytime maximum of 960 minutes per day. Table 1 summarizes the current technical capabilities at a larger facility (100 birds in a -300 sqft facility, setup shown in FIG.30). As detailed in Table 1, the technology tracks 100 birds with an average of 204 minutes of tracking per day. The technology can provide the resolution and interoperability for research applications.Table 1. Daily tracking-performance metrics (averaged across the study period).Metric Result Note- 99 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Object-ID Performance (F1 score) 0.995 YOLOv8n-based model Mean trajectory length 204 min per bird per day Min / Max trajectory length 4 min / 513 min per bird per day Birds tracked > 60 min per day 71 out of 100 Avg. RFID pings per day >2,000 6 antennas on water line Avg. distinct RFID birds per day 98An Unscented Kalman Filter

[0423] Commercial-scale poultry houses present unique tracking challenges: (i) non-linear optics — wide-angle ceiling cameras warp perspective and birds walk on stepped litter surfaces; (ii) dense crowds — hundreds of visually similar birds occlude one another; (iii) cost constraints — the hardware budget favors low-resolution sensors. An Unscented Kalman Filter (UKF) is uniquely well matched to this environment. An implementation is described in FIG. 31. Unlike a linear Kalman filter, the UKF propagates a set of sigma points through the full, non-linear camera projection, preserving second-order accuracy. Detections from overlapping cameras are fused into a single, centimeter-accurate estimate per bird — even when the view is heavily distorted or partly obscured.Tailored motion models and robust data association

[0424] The system alternates between a constant-velocity model that smooths normal walking and a coordinated-turn, constant- speed (CTCS) model for sharper changes in heading. Process-noise parameters are tuned from legacy 100-bird footage and refined online via active learning. Each frame the UKF outputs a predicted centroid and uncertainty ellipse. A Hungarian assignment then matches predictions to new triangulated detections. Occluded tracks are “parked” (uncertainty grows but the ID is retained) and can be resurrected if a matching detection re-appears within 100 frames. This logic preserves global IDs through short occlusions and camera dropouts — critical for computing activity budgets and linking trajectories to RFID pings.

[0425] This combination of poultry-tracking-tailored models creates an idea framework for tracking, but parameters of the UKF can be challenging to optimize. The following section presents an activelearning approach to parameter fine-tuning.- 100 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601Active Learning: Tracker Tuning and Precision Chicken Identification

[0426] Explicit expertise in leveraging active learning is integrated to uncover optimal parameters of high-dimensional systems with complex topologies, necessary to quickly tune both the Kalman Filter tracker and object ID models in general and for novel facilities and environments.Active Learning Improves Precision Kalman Filter Tracking 4-10x

[0427] As illustrated in FIG.32, a Bayesian-optimization active learning loop is used to calibrate the poultry-specific Kalman Filter. The active learning optimizes performance by rewarding long, continuous tracks while penalizing identity switches. After thousands of iterations, the loop converges on an optimal set θoptthat delivers robust, facility-agnostic tracking. This optimized Kalman Filter then produces longer, richer truth trajectories, which in turn seed subsequent active-learning cycles — continuously expanding the dataset and refining both tracker precision and chicken identification accuracy. Currently, the modest datasets have been able to see this approach improve performance by 4x, but improvements of 10x or more are feasible with this active learning construction.Active Learning for Precision Chicken Identification

[0428] Building on the Bayesian optimization loop for Kalman-filter tuning, a complementary activelearning pipeline is implemented to continually improve the chicken identification models using both real and synthetic data (FIG.33).Synthetic Data and Computer Vision Generalization

[0429] The Virtual Agriculture Environment (VAE) simulates poultry houses — with customizable cameras, lighting, textures, and flock behaviors — to produce photo-realistic scenes and pixel-perfect labels. By injecting these edge-case synthetic frames into the active- learning loop, the models are fine-tuned entirely in silico. The models can then be deployed on real farms. Real-world performance can be used to update the simulator — driving rapid, continuous improvements well beyond generic data collection. Original scene diversity first added just +0.02 to the object-ID model’s Fl when mixed with real data; with improved rendering, domain randomization, and lighting, providing up to +0.10 in specific conditions. Ongoing simulator refinements will keep closing the sim-to-real gap and driving higher Fl scores.Computer Vision Generalization

[0430] The simplicity of chicken identification tasks has allowed the model to perform in new environments at Fl > 0.95 in zero shot applications (trained on all other data and synthetic). This is even greater between two white chicken environments with an Fl > 0.97. Zero-shot performance is- 101 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601expected to continue to increase with more data and, with only a handful of active learning loops, can meet performance thresholds exceeding Fl > 0.995.Scaling: Tackling Resolution, Compute, and Real-Time Tracking Efficiency

[0431] Equation 1 describes the computational scaling question that must be resolved, where facility compute capacities, NFLOP / second, must be larger than the product of the Computer Vision Model Efficiency, Camera Design Layout, Tracking Model Efficiency, and Data Compression. These components can be improved to permit just-in-time tracking for 1,500 birds using a single NVIDIA RTX 5090 (a $1,999 cost with 400 TFLOPs compute). FIG. 34 presents three key developments to meeting this challenge.

[0432] Equation (1). NpLOP / second ^FLOP / pixel Npixel / bird bird / frame frame / second H Downsa piing_Factor-’ where N FL P / second represents Compute, N FLOP / pixel Npixei / bird represents Computer Vision Model; Nbird / framerepresents Camera Design; Nframe / secondrepresents Tracking Model; and ηDownsampling_Factorrepresents Data Compression.Expected Results

[0433] A year-long, 1,500-bird field trial will be conducted at a commercial poultry house to capture >30 billion behavior and health data points and an expected 150 mortality events or less with a dualcamera network — 70 high-resolution IP cameras and 140 low-cost USB units. An online activelearning loop will continuously refine detection, tracking, and phenotype models, allowing quantification of resolution-versus-cost trade-offs and proof that models trained on the IP backbone transfer to commodity hardware. The outcome will be the world’s largest Al poultry dataset and a validated blueprint for scaling individualized monitoring to 40,000-bird houses at just $0.05 per bird per cycle. Table 2 details the additional expected outcomes from the project.Table 2. Expected outcomes across four project objectives.Objective 1: Develop, deploy, & test a large-scale, individual-bird activity-tracking system Outcome1.1 Poultry-specific CV model throughput and accuracy meet project targets (Fl > 0.995).1.2 Kalman-filter tracker validated for poultry dynamics (MOTA > 0.70, ID switch < 10%).1.3 Edge RTX 5090 hardware feasibility for real- time tracking is demonstrated.Objective 2: Relate individual activity to health phenotypes2.1 Twelve weeks of per-bird activity correlated with observed health and livability outcomes.2.2 Behavioral effects of different management conditions quantified using tracker data.- 102 - 4900-3191-0147.1WSGR Docket No.: 69213-701.6012.3 Minimum predictive trajectory window for health assessment identified.Objective 3: Just-in-time detection of deteriorating health3.1 Early-warning classifier validated for just-in- time detection of health decline (Fl > 0.90, lead-time > 24h).3.2 Actionability window between alert and intervention characterized.3.3 Statistical convergence of cumulative health / activity logs analyzed per bird.Objective 4: Demonstrate applicability and impact for trials and commercial operations4.1 Individual-based insights shown to outperform bulk, flock-level statistics in trials.4.2 Economic and welfare impact of scaled monitoring system quantified.

[0434] Preparation and installation (Month 0 4). The first 4 months comprise two parallel sprints: Algorithm R& D - Refine bird- detectors and Kalman / GRU motion models on legacy 100-bird footage plus VAE-generated edge cases; target Fl > 0.995 on down-sampled 720 p by week 12. Hardware& commissioning - Lock camera layout, order parts, bench-test node PCs (Intel i9 + RTX 5090) and a 400 TB NAS, then install cabling, PoE switches, cameras, and RFID. Finish with intrinsic / extrinsic calibration and a 24 h dry-run recording.

[0435] Facility hardware stack. The 1,500-bird house will host two redundant sets of cameras: 70 proven 1080 p IP cameras and 28 Pi nodes driving 140 USB 640 x 480 cameras to vet a low-cost, scalable, easy-to-install path. Supporting sensors include 50 RFID portals, 12 inline scales, and 8 temp / RH / CO2 probes. All video feeds via CAT-6A into 448-port PoE++ switches, aggregated by a 10 Gb link to an edge server (RTX 5090, 128 GB RAM, 4 TB NVMe). Long-term storage: 24-bay NAS (24 x 20 TB, RAID-6) holding a 12- week 720 p 15 fps buffer plus per-bird CSV logs. A VPN connects the site to Prophet Al’s Boston HQ for nightly encrypted backups and remote model updates.FIG. 36 illustrates the layout.

[0436] Experimental phase (Month 5-12). After a 2-week shakedown, the trial proceeds with three 12- week phases, yielding an uninterrupted 36- week dataset with >30 billion positional data points.

[0437] Phase 1 - Baseline collection (M5-6): harvest ~20 TB of trajectories from both camera layers to complete detector / tracker training and baseline low-cost system performance testing.

[0438] Phase 2 - Model building (M7-9): build predictive phenotype models, benchmark resolution-versus-cost trade-offs for each camera system, and optimize Pi-node compute.

[0439] Phase 3 - Live health alerts (M10-12): deploy just-in-time alerts and an operational dashboard on the low-cost network for full-house monitoring.- 103 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0440] The high-resolution phenotyping / monitoring technology will lead to supply chain transparency, ensure sustainably met protein needs, improve US food security, and expand Al automation across Ag. It will also lead to greater transparency in the farm-to-fork supply chain. The technology will provide health monitoring to growers and retailers, who are increasingly committed to providing affordable food with high health and welfare standards, looking to drive greater transparency in the farm-to-fork supply chain and build consumer trust.

[0441] Just-in-time monitoring provides clear, immutable records to mitigate health concerns. These records enhance accountability and traceability, also simplifying product recalls and aiding in disease identification. They strengthen relationships between retailers and suppliers. Ensuring compliance with farming regulations and early detection of issues to improve product quality and reduce negative feedback directed at retailers. They can build trust with a consumer increasingly interested in animal welfare, as records can be shared to differentiate a grower’s commitment to high-quality lives of their animals.

[0442] For growers and retailers committed to these areas, and those outlined in the Better Chicken Commitment, the technology can help farmers demonstrate compliance. Just-in-time data capture is crucial for improving bird welfare and building consumer trust.

[0443] Reducing worker stress and trauma. Continuous, automated welfare monitoring also protects the people who care for livestock. Farm staff often experience moral stress when forced to manage animal suffering from undetected disease, lameness, or overcrowding. By flagging welfare issues early and guiding rapid, data-based interventions, the technology lessens the frequency and severity of difficult tasks (e.g., culling, mass depopulation). This reduces psychological strain, lowers turnover, and helps producers retain skilled labor while maintaining higher welfare standards.

[0444] The technology can help ensure world protein needs are sustainably met. The world needs more chicken. Chicken promises to fill the world’s protein gap and remove gigatons (GT) of livestock emissions. The technology furthers these missions, both directly and indirectly. The technology can aid in fulfilling a “triple obligation” to environmental, financial (farmer), and social (animal welfare) sustainability. The technology is expected to achieve at least a 3% reduction in chicken mortality in the U. S. via breeding / health insights and improved production management, potentially saving over 3 MMT of CO2, $900 million in revenue, and 270 million birds annually (4 times globally). The technology can also allow poultry to meet consumer shifts from red to white meat. Transitioning the 72 MMT of consumed beef to poultry would save a whopping 3.8 GT of CO2annually.- 104 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601

[0445] The tracking technology and synthetic data approach can bring Al automation to several agricultural sectors. Poultry health tracking can deliver high-resolution, entity-level phenotyping and monitoring. While livestock (cattle, swine, fish, etc.) are natural first adopters, the same approach can be tuned to any other systems to maintain object-level fidelity (for example, surveys of row crops). Labor shortages are widespread across agriculture, and the technology of the present disclosure can fill that gap. From livestock, where fish endure staggering 20%+ mortality rates, to greenhouses, where mistimed harvests go to waste, the technology can be readily adapted to operate across a consistently growing agricultural market, shown in FIG. 2.

[0446] The Kalman-filter tracker follows each chicken continuously, yielding individual-level trajectories paired with feeding and drinking times, weight, gait parameters, and early-warning health signals that no other system can resolve. Pairing trajectories with phenotypes is critical to build causal and effective insights. For example, a health critical, low-activity bird at different weights requires different actions. All phenotypes, like health, are conditional. Tracking illuminates those conditions.

[0447] Redefining the unit of analysis from the flock to the chicken is important for improving the poultry sector (and beyond). That chicken-resolution insight powers more accurate health projections, targeted interventions, and predictive recommendations — moving toward truly data-driven bird management.Terms and Definitions

[0448] While preferred embodiments of the present disclosure have been shown and described herein, such embodiments are provided by way of example only. It is not intended that the present disclosure be limited by the specific examples provided within the specification. While the present disclosure has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions may occur without departing from the present disclosure. Furthermore, it shall be understood that all aspects of the present disclosure are not limited to the specific depictions, configurations, or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the present disclosure described herein may be employed in practicing the present disclosure. It is therefore contemplated that the present disclosure shall also cover any such alternatives, modifications, variations, or equivalents. It is intended that the following claims define the scope of the present- 105 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601disclosure and that systems, methods and structures within the scope of these claims and their equivalents be covered thereby.

[0449] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this present disclosure belongs.

[0450] As used herein, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0451] As used herein, the term “about” in some cases refers to an amount that is approximately the stated amount.

[0452] As used herein, the term “about” refers to an amount that is near the stated amount by 10%, 5%, or 1 %, including increments therein.

[0453] As used herein, the term “about” in reference to a percentage refers to an amount that is greater or less the stated percentage by 10%, 5%, or 1%, including increments therein.

[0454] As used herein, the phrases “at least one”, “one or more”, and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.- 106 - 4900-3191-0147.1

Claims

WSGR Docket No.: 69213-701.601CLAIMSWhat is claimed is:

1. A computer-implemented method, comprising:(a) receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects;(b) processing the synthetic data to train a machine learning (ML) model to yield a trained ML model;(c) using the trained ML model to predict a plurality of attributes of a physical environment; and(d) determining, based at least on the predicting in (c), one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent.

2. The method of claim 1, further comprising generating the synthetic data, wherein the synthetic data comprises a log, wherein the log comprises a plurality of attributes of each virtual sensor, virtual agent, or virtual object.

3. The method of claim 2, further comprising using the log to automatically train the trained ML model, wherein the automatic training comprises supervised training or unsupervised training.

4. The method of claim 3, wherein the physical environment comprises a plurality of (i) real sensors, (ii) real agents, or (iii) real objects.

5. The method of claim 4, wherein the plurality of attributes comprises attributes associated with (i) each virtual sensor or real sensor, (ii) each virtual agent or real agent, or (iii) each virtual object or real object.

6. The method of claim 4, wherein the trained ML model comprises a sensor-attribute model configured to (i) analyze data from the plurality of virtual sensors or real sensors and (ii) determine at least one attribute of the plurality of attributes.

7. The method of claim 6, wherein generating the sensor-attribute model comprises:(a) receiving sensor data from (i) the plurality of virtual sensors, (ii) the plurality of real sensors, or both (i) and (ii);(b) processing the sensor data to determine attributes of the plurality of virtual agents or real agents; and(c) updating the synthetic data from the processing in (b) for use by the trained ML model based at least on the determined attributes.- 107 - 4900-3191-0147.1WSGR Docket No.: 69213-701.6018. The method of claim 7, further comprising updating the sensor-attribute model based at least on training the sensor-attribute model with the updated synthetic data.

9. The method of claim 7, further comprising generating the synthetic data, wherein the synthetic data comprises a log, wherein the log comprises of a plurality of attribute data in the virtual environment.

10. The method of claim 9, further comprising using the log to automatically train the sensorattribute model, wherein the automatic training comprises supervised training or unsupervised training.

11. The method of claim 6, wherein the trained ML model further comprises an attribute-entity model configured to uniquely associate attributes to (i) each virtual agent, (ii) each real agent, or both (i) and (ii).

12. The method of claim 11, wherein generating the attribute-entity model comprises:(a) receiving determined attributes from the sensor-attribute model and applying the determined attributes to (i) the virtual sensors, (ii) the real sensors, or both (i) and (ii);(b) processing the determined attributes to determine unique attributes for (i) each virtual agent, (ii) each real agent, or both (i) and (ii); and(c) updating the synthetic data from the processing in (b) for use by the trained ML model based at least on the determined unique attributes.

13. The method of claim 12, further comprising updating the attribute-entity model based at least on training the attribute-entity model with the updated synthetic data.

14. The method of claim 12, further comprising generating the synthetic data, wherein the synthetic data comprises a log, wherein the log comprises a plurality of entity data and a plurality of attribute data in the virtual environment.

15. The method of claim 14, further comprising using the log to automatically train the attributeentity model, wherein the automatic training comprises supervised training or unsupervised training.

16. The method of claim 11, wherein the trained ML model further comprises an entity-attribute projection model configured to predict the plurality of attributes at one or more future time periods with a predetermined confidence level.

17. The method of claim 16, wherein generating the entity -attribute projection model comprises:(a) receiving time-history data from (i) the virtual environment, (ii) the physical environment, or both (i) and (ii);- 108 - 4900-3191-0147.1WSGR Docket No.: 69213-701.601(b) processing the time-history data to predict an attribute over time for (i) each virtual agent, (ii) each real agent, or both (i) and (ii); and(c) updating the synthetic data from the processing in (b) for use by the trained ML model based at least on actual attributes or the predicted attributes.

18. The method of claim 17, further comprising updating the entity-attribute projection model based at least on training the entity -attribute projection model with the updated synthetic data.

19. The method of claim 17, further comprising generating the synthetic data, wherein the synthetic data comprises a log, wherein the log comprises the time-history data in the virtual environment.

20. The method of claim 19, further comprising using the log to automatically train the entityattribute projection model, wherein the automatic training comprises supervised training or unsupervised training.

21. The method of claim 16, wherein the trained ML model comprises each of (i) the sensor-attribute model, (ii) the attribute-entity model, and (iii) the entity-attribute projection model.

22. The method of claim 4, wherein the virtual environment or the physical environment comprises an agricultural development facility, a production facility, or a processing facility for use with livestock, animals, aquaculture, row crops, greenhouse produce, greenhouse horticulture, or other agricultural sectors.

23. The method of claim 4, wherein the plurality of virtual agents or real agents comprises animals, plants, fungi, protists, or bacteria.

24. The method of claim 4, further comprising using the method for genetic selection of a virtual agent or a real agent determined to exhibit improved attributes over a baseline virtual agent or real agent.

25. The method of claim 4, further comprising using the method to monitor health attributes in real time for each real agent.

26. The method of claim 4, wherein the one or more actions comprise adjusting (i) environmental parameters of the physical environment, (ii) feeding, watering, or other management parameters for the plurality of real agents, or (iii) density of the plurality of real agents.

27. The method of claim 4, further comprising using a ML model to generate the virtual environment and the plurality of attributes of the virtual sensors, agents, and objects from data of the real sensors, agents, and objects received from the plurality of real sensors.- 109 - 4900-3191-0147.1WSGR Docket No.: 69213-701.60128. A system comprising at least one processor and instructions executable by the at least one processor to cause the at least one processor to perform operations comprising:(a) receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects;(b) processing the synthetic data to train a machine learning (ML) model to yield a trained ML model;(c) using the trained ML model to predict a plurality of attributes of a physical environment and a plurality of attributes of real agents in the physical environment; and(d) determining, based at least on the predicting in (c), one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent.

29. A computer- implemented method comprising processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories.

30. A computer-implemented method of training a neural network, comprising:(a) collecting (i) one or more trajectories of one or more organisms and (ii) one or more biological or behavioral features of one or more organisms throughout the one or more trajectories;(b) processing the (i) one or more trajectories of one or more organisms and (ii) the one or more biological or behavioral features of one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms; and(c) training the machine learning model to reduce a loss function that quantifies an error associated with the one or more predictions of the one or more phenotypes compared to one or more ground-truth values of the one or more phenotypes.

31. A computer- implemented method of predicting one or more phenotypes of one or more organisms, comprising:(a) processing (i) one or more trajectories of the one or more organisms and (ii) one or more biological or behavioral features of the one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms.- 110 - 4900-3191-0147.1WSGR Docket No.: 69213-701.60132. A system comprising one or more processors independently or collectively configured for processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories.

33. A system comprising one or more processors independently or collectively configured for training a machine learning model by:(a) collecting (i) one or more trajectories of one or more organisms and (ii) one or more biological or behavioral features of one or more organisms throughout the one or more trajectories;(b) processing (i) the one or more trajectories of one or more organisms and (ii) the one or more biological or behavioral features of one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms; and(c) training the machine learning model to reduce a loss function that quantifies an error associated with the one or more predictions of the one or more phenotypes compared to one or more ground-truth values of the one or more phenotypes.

34. A system comprising one or more processors independently or collectively configured for predicting one or more phenotypes of one or more organisms by:(a) processing (i) one or more trajectories of the one or more organisms and (ii) one or more biological or behavioral features of the one or more organisms throughout the one or more trajectories to generate one or more predictions of the one or more phenotypes of the one or more organisms.

35. A computer program product comprising computer-executable instructions for:(a) receiving synthetic data generated using a virtual environment, wherein the virtual environment comprises a plurality of (i) virtual sensors, (ii) virtual agents, or (iii) virtual objects;(b) processing the synthetic data to train a machine learning (ML) model to yield a trained ML model;(c) using the trained ML model to predict a plurality of attributes of a physical environment; and(d) determining, based at least on the predicting in (c), one or more actions for a user to perform thereby affecting at least one attribute of at least one real agent.- Ill - 4900-3191-0147.1WSGR Docket No.: 69213-701.60136. A computer program product comprising computer-executable instructions for processing one or more trajectories of one or more organisms to track one or more identifiers of the one or more organisms throughout the one or more trajectories.

37. A system comprising a digital twin of an agricultural environment comprising a plurality of virtual agents that tracks a plurality of real entities at an entity-level resolution.- 112 - 4900-3191-0147.1

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