System and associated method for determining parameter settings for an enclosed habitat - Patents.com

Abstract

A cloud-based management system for indoor growing devices. The management system can be configured to monitor production, yield, and food quality of multiple devices and adjust growing conditions of individual devices to improve production, yield, and food quality.

Description

[Technical field]

[0001] The present invention relates to a system and associated method for determining parameter settings for an enclosed habitat. [Background technology]

[0002] This application claims priority to U.S. Application No. 63 / 236,505, filed August 24, 2021, and entitled “SYSTEM FOR DETERMINING PARAMETER SETTINGS FOR AN ENCLOSED GROWING ENVIRONMENT,” which is incorporated by reference in its entirety herein.

[0003] The use of home gardens and microgardens in apartment complexes and neighborhoods has increased across the United States in recent years in response to produce deserts, where access to fresh produce is limited in densely populated areas. More consumers are looking to grow fresh produce and herbs at home to not only provide fresh produce but also limit the preservatives and chemicals used in large grocery stores. Depending on the climate, homeowners may be limited to indoor systems for growing fresh produce and herbs. However, most indoor systems have limited space, provide uniform growing conditions for all produce and herbs, and often do not result in optimal conditions for all the produce and herbs the homeowner produces. Additionally, homeowners often lack the education and time to properly maintain optimal growing conditions for each individual species or plant type. [Brief description of the drawings]

[0004] The detailed description will be set forth with reference to the accompanying drawings, in which the leftmost digit(s) of a reference number identifies the figure in which the reference number first appears, and the use of the same reference number in different drawings indicates similar or identical components or features.

[0005] [Figure 1]FIG. 1 is an example block diagram of a management system for determining parameters of plants associated with an enclosed growing environment or apparatus. [Diagram 2] FIG. 1 is an exemplary block diagram of an architecture of a management system for determining parameters of plants associated with an enclosed growing environment or apparatus. [Diagram 3] FIG. 1 is an example block diagram of an architecture associated with a management system for determining parameters associated with an enclosed habitat or apparatus. [Figure 4] FIG. 1 is an example block diagram of an architecture associated with a management system for determining parameters associated with an enclosed habitat or apparatus. [Diagram 5] 1 is an example flowchart illustrating an example process for updating policies or configurations associated with a management system, according to some implementations. [Figure 6] 1 is an example flowchart illustrating an example process for updating ordering instructions associated with a management system, according to some implementations. [Figure 7] 1 is an example flowchart illustrating an example process for updating parameters associated with a management system, according to some implementations. [Figure 8] FIG. 1 is an exemplary diagram of cloud-based services associated with a management system, according to some implementations.

[0006] The drawings depict various embodiments for purposes of illustration only. Those skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Described herein are systems and methods related to automating, optimizing, and customizing parameters for controlling enclosed growing appliances (such as microgardens) at home. For example, a management system may be communicatively coupled to one or more enclosed growing appliances. As described herein, in some implementations, the appliance may provide an isolated enclosure configured to provide stable and controlled environmental conditions physically separated from conditions within a surrounding environment (such as a home or apartment complex). For example, the appliance may include a planting column or tower within the enclosure. The planting column may include multiple receptacles configured to receive individual cartridges. The planting receptacles may be arranged in both vertical and horizontal columns around the planting column. For example, in one particular example, the planting column may include 20 columns and 5 rows of planting receptacles. In some cases, the planting receptacles may be staggered between the columns, for example, each column having one planting receptacle in every other row. In such cases, staggering the planting receptacles allows the device to not only monitor individual plants, but also allows each plant adequate room to grow.

[0008] In some cases, the receptacle may be pre-sized to accept a pre-prepared and / or pre-packaged seed cartridge. In this way, a user may insert the cartridge into the receptacle for a simple, streamlined planting process. The seed cartridge may be a cartridge that contains seeds of a desired plant, fertilizer, and other media (such as a growing medium) in some cases. The seed cartridge may be of uniform size and dimensions and may include openings for receiving water and other nutrients through the planting column.

[0009] In some implementations, the management system may be configured to receive sensor data from individual devices (such as temperature data, image data, air quality data, light data, water quality data, etc. associated with the devices), user inputs and settings from user devices associated with the owners of the devices, cartridge data associated with plants being grown or inserted in the devices (such as plant type, cartridge manufacturer, cartridge installation, planting date, etc.), and third party data. The management system may then utilize the received data to determine growth parameters for each of the individual devices and / or for individual cartridges or plants within the devices.

[0010] For example, the closed growing apparatus may be configured to provide a closed growing environment for home and indoor cultivation of plants and fungi, flowers, fruits, vegetables, produce, mushrooms, and / or herbs. The system may provide an isolated enclosure configured to provide stable and controlled environmental conditions that are physically separated from conditions within the surrounding environment (e.g., a home or apartment complex) in some implementations. However, unlike traditional home gardening systems that provide uniform lighting and temperature, the enclosures discussed herein may provide active monitoring (e.g., sensor data collection) and adaptive environmental conditions (based on parameters received from a management system).

[0011] In some particular implementations, the management system may be configured to monitor individual plants within the growing environment by processing (e.g., segmenting, classifying, clustering, etc.) the sensor data received from each individual device. In this manner, the management system may determine the location, size, health, growth stage, type or species, etc. of individual plants within the device. The management system may also store or determine user or user preferences associated with the device, such as plant flavors, sizes, types, recipes, seasonings, cooking or preparation styles, food pairings, etc., based on user data or input received via the user device and associated downloadable or web-hosted applications.

[0012] The management system may also determine characteristics of a particular plant inserted into the device based on the cartridge data received from one or more third-party systems. In some cases, the cartridge data may include a chain of custody, such as via a blockchain, such that the life cycle of each cartridge may be monitored. For example, cultivation facilities, packing facilities, transportation or shipping, sales locations, and distribution locations may all be tracked as the seeds / cartridges move from one location to the next. In some implementations, the management system may track historical data associated with plants originating from different facilities via the cartridge data. In some cases, one lineage of a plant harvested or grown at a particular facility may perform better (e.g., grow faster or larger, have better color, may have a more desirable or more intense flavor, etc.), and the management system may use the cartridge data along with sensor data received from the device hosting the plant and / or user data from a user consuming the plant to track the location of the facility or the plant lineage. In some cases, the management system may also detect and / or determine characteristics of the seed cartridge using captured sensor data, such as a code, image, or icon present on the cartridge (detected during or after insertion), a change in color of the cartridge, the temperature of the cartridge, etc.

[0013] In some particular examples, the sensor data may also include environmental data associated with the physical environment outside the enclosure of the device (e.g., temperature, humidity, air quality, lighting, water, etc.). In these examples, the management system may also utilize third-party external environmental data to determine global and / or local policies and / or parameters associated with the device and / or plants. In some cases, the external environmental data may be received from smart and / or IoT-enabled devices in the environment, such as smart thermostats, smart lights, smart fire detectors, and / or other IoT-enabled systems in the external environment.

[0014] In one particular example, the management system may utilize the cartridge data along with associated plant growth data determined from the sensor data provided by the device to track expected germination rates of cartridges from a particular facility, supplier, grower, and / or manufacturer. For example, a manufacturer may provide or agree to an expected germination rate when engaged to provide cartridges to a device user on behalf of the management system. In this example, the management system may determine the actual germination rate of cartridges produced by a particular facility, supplier, grower, and / or manufacturer and determine whether the particular facility, supplier, grower, and / or manufacturer met or exceeded the expected germination rate. If a particular facility, supplier, grower, and / or manufacturer does not meet or exceed the expected germination rate, the particular facility, supplier, grower, and / or manufacturer may be alerted (e.g., via periodic reports) of the loss and the number of additional cartridges that the particular facility, supplier, grower, and / or manufacturer is expected to deliver under the agreed upon terms. In some cases, the management system may also determine particular facilities, suppliers, growers, and / or manufacturers to continue, renew, expand, or reduce orders with based on the determined germination rates (e.g., facilities with higher than expected germination rates may be requested to increase production, and facilities with lower than expected germination rates may be requested to reduce production).

[0015] In such implementations, the management system may generate global (e.g., across devices) and / or local (e.g., per device or cartridge location) policies and parameters to improve production, yields, food quality, ease of use, and general user experience associated with ownership and utilization of the devices described herein. For example, by processing sensor data, user data, cartridge data, and / or other third party data, the management system may generate and provide custom lighting (e.g., exposure length, focal length, temperature, specific wavelengths, intensity, amount, etc.), tailored growing conditions such as temperature, humidity, water, etc. for individual devices and / or individual plants within a particular device.

[0016] In one specific example, the system may also use a machine learning model or network to perform object detection and classification on the plants, determine parameters or settings, and generate policies. For example, one or more neural networks may generate any number of learned inferences or heads. In some cases, the neural network may be a trained network architecture that is end-to-end. In one example, the machine learning model may include segmenting, clustering, and / or classifying extracted deep convolutional features of the sensor data into semantic data (e.g., stiffness, light absorption / reflectance, color, health status, life stage, etc.). In some cases, the relevant information (Truth) outputs the model in the form of a semantic pixel-by-pixel classification (e.g., leaf, stem, fruit, vegetable, bug, rot, etc.).

[0017] In one particular example, the end-to-end network architecture may be a convolutional neural network (CNN) that receives multiple inputs and outputs end results such as updated policies, recommended recipes, recommended plant purchases or seed cartridges, orders to various third parties (e.g., growers, cartridge manufacturers, etc.). In some cases, the inputs to the end-to-end network may include third-party data, seed cartridge data, user data from one or more users (e.g., user preferences, user-specific settings, etc.), device data or sensor data from one or more devices (e.g., device internal and external environmental data, plant data, image data, active receptacles or receptacles containing seed cartridges, etc.). For example, in one embodiment, the management system may input user-specific data (e.g., user data and device data associated with a particular user) along with current third-party data as multiple heads into a trained end-to-end network that outputs one or more of plant health data, produce orders (e.g., seed cartridges), recommendations to the user (e.g., adjustment settings, harvesting, plant selection, etc.), etc. It should be appreciated that the output of the end-to-end network may be directed, provided, or transmitted to a variety of parties, including suppliers, growers, user electronic devices, equipment, manufacturers, point of sale systems, and the like.

[0018] In some cases, based on the determined policies and parameters, the management system may be configured to order on behalf of or for users associated with different devices. For example, if a user appears to prefer one supplier over another, or one type of plant over another (e.g., one type of lettuce over another type of lettuce), the management system may update or change the supplier to select the supplier that the user is determined to prefer.

[0019] In some specific examples, the management system may utilize a multi-armed bandit technique to generate parameters, settings, and / or policies based on the received data and one or more control parameters, as described above. In other cases, any type of machine learning may be used consistent with the present disclosure. For example, machine learning algorithms may include, but are not limited to, regression algorithms (e.g., ordinary least squares regression (OLSR), linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines (MARS), locally estimated scatterplot smoothing (LOESS)), instance-based algorithms (e.g., ridge regression, least absolute shrinkage and selection operator (LASSO), Elastic net, least-angle regression (LARS)), decision tree algorithms (e.g., classification and regression trees (CART), iterative dichotomiser 3 (ID3), chi-squared automated interaction detection (CHAID), decision stump, conditional decision tree), Bayesian algorithms (e.g., naive Bayes, Gaussian naive Bayes, multinomial naive Bayes, average one-dependence Bayes, etc.), and / or metric-based algorithms (e.g., metric-based regression ... estimators), Bayesian belief networks (BNNs), Bayesian networks), clustering algorithms (e.g., k-means, k-medians, expectation maximization (EM), hierarchical clustering), association rule learning algorithms (e.g., perceptrons, backpropagation, Hopfield networks, RBFNs (Radial Basis FunctionNetwork), deep learning algorithms (e.g., Deep Boltzmann Machines (DBM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), stacked autoencoders), dimensionality reduction algorithms (e.g., Principal Component Analysis (PCA), Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Sammon Mapping, Multidimensional Scaling (MDS), Projection Pursuit, Linear Discriminant Analysis (LDA), Mixture Discriminant Analysis (MDA), Quadratic Discriminant Analysis (QDA), Flexible Discriminant Analysis (FDA)), ensemble algorithms (e.g., Boosting, Bootstrap Aggregation (Bagging), AdaBoost, Stacked Generalization (Blending), Gradient Boosting Machines (GBM), Gradient Boosted Regression Trees (GBRT), Random Forests), SVM (Support Vector Machines), supervised learning, unsupervised learning, semi-supervised learning, etc. Further examples of architectures include neural networks such as ResNet50, ResNet101, VGG, DenseNet, PointNet, etc. In some cases, the system may also apply Gaussian blur, Bayesian functions (Naive Bayes), color analysis or processing techniques, and / or combinations thereof.

[0020] As described herein, an exemplary neural network is a biologically inspired algorithm that passes input data through a series of connected layers to generate an output. Each layer in a neural network can include another neural network, or can include any number of layers (convolutional or not). As can be understood in the context of this disclosure, a neural network can utilize machine learning, which can refer to a broad class of such algorithms in which an output is generated based on learned parameters.

[0021] 1 is an exemplary block diagram of a management system 102 for determining parameters of a plant associated with a closed growing environment or apparatus. In the current example, the management system 102 may receive sensor data 104 from the apparatus 106, cartridge data 108 from the apparatus 106, supplier systems 120 (e.g., manufacturers, growers, assembly contractors, parts suppliers, etc.), and / or other third party systems 110, as well as user data 112 from one or more users 114. As discussed above, the sensor data 104 may include temperature data, image data, light data, etc. associated with the apparatus 106. In some cases, the sensor data 104 may also include water data, such as incoming water quality data, segregated water data (e.g., water that has been segregated by the apparatus 106 to remove, e.g., heavy metals, etc., before being introduced into the recirculating water supply), and dispensed or recirculating water data. The sensor data 104 may also include air quality data, which may include multiple stages of air, such as incoming air supply quality data, segregated air data (e.g., air that has been segregated by the device 106 before being introduced into the device's air supply), and distributed or recirculated air data.

[0022] The user data 112 may include settings from a user device associated with the user 114, such as preferences of the user 114 (e.g., plant flavor, plant color, leaf size at time of consumption, plant age or life cycle at time of consumption, etc.), desired plant size, desired plant type (species or family), favorite recipes, favorite seasonings, cooking or cooking style, food pairings, etc. The user data 112 may include data from a third party application 110 (social media application, marketplace application, smart home application, etc.) associated with the user 114, such as details of the user 114 (e.g., family size, culture, age, location, etc.). The cartridge data 108 may include plant species, family, expected germination rate, cultivation facility, planting date, seed insertion date, date of cartridge placement in the device 106, etc.

[0023] In some examples, the management system 102 may also receive third-party data 132 from third-party applications 110. The third-party data 132 may include research data, marketplace data, smart home data (e.g., pantry or storage data, environmental data, smart appliance data, etc.), health data, genetic data, historical data, mark sales data, advertising data, currency exchange data, government data, social media data, web crawler data, agricultural partners, insurance data, complementary food data, meal kit plans, grocery data, customer data, subscription data, among other types of data.

[0024] The third party applications and systems 110 may include businesses, universities, research facilities, other growers, social media, government agencies, marketplaces, delivery systems, ordering systems, health systems, wearable systems, and the like. For example, the management system 102 may utilize the third party data and sensor data 104 from the smart devices to send a report / request 122 to a grocery delivery system (e.g., the second third party system 110). In this example, the report / request 122 may include an order for delivery of food that may complement plants that are near harvesting. In some particular examples, the report / request 122 may include a specific delivery date that coincides with optimal data for harvesting. As another illustrated example, the third party data 132 may include individual and / or aggregated (and de-personalized) health data. In this example, the management system 102 may utilize the health data to determine dietary suggestions and / or parameters 116 for the devices 106 to, for example, improve an individual's vitamin C deficiency. In some cases, when the management system 102 sends the report / request 122 to the third party system 110, it may include an order for plants that have specific nutritional benefits based on the health data.

[0025] The management system 102 may then utilize the received data 104, 108, and 112 and historical and / or aggregated data (by plant, condition, device, etc.) to determine growth policies and parameters 116 for each individual device 106 and / or for each individual cartridge or plant within the device 106. In some particular implementations, the management system 102 may be configured to monitor individual plants within the growing environment by processing (e.g., segmenting, clustering, classifying, etc.) the sensor data 104. For example, the management system 102 may determine the location, size, health, growth stage, type or species, etc. of individual plants within the device 106. The management system 102 may also determine characteristics of the particular plant inserted into the device 106. In this manner, using the sensor data 104, the management system 102 may determine the properties and characteristics of the plants as they grow within the device 106.

[0026] The management system 102 may then utilize the cartridge data 108 along with the characteristics and features of the plants in the multiple devices to update and / or determine policies and / or parameters 116 (e.g., lighting, humidity, temperature, water, etc.) for each individual plant in the device 106. In some cases, the management system 102 may aggregate sensor data 104 across multiple devices located in a given geographic area, having similar external environmental conditions (e.g., conditions outside the housing of the device 106 are within thresholds), with similar internal environmental conditions (e.g., conditions within the housing of the device 106 are within thresholds, such as the same plants, similar plant arrangements, cartridges are from the same supplier, grower, facility, manufacturer, and / or geographic area, etc.).

[0027] If the management system 102 determines that a different plant, plant family, arrangement of plants within the device 106, etc. performs better and / or is healthier under particular conditions, the management system 102 may update or adjust the policies, configurations, and / or parameters 116 that control the characteristics and / or growing conditions of the device 106.

[0028] In some cases, the management system 102 may operate by generating simulations of mirrored configuration systems and / or particular devices 106. In this manner, the management system 102 may test or simulate the performance of plants with various parameters 116 and / or configurations before globally applying them to multiple different devices and / or mirrored devices 106 with matching criteria (e.g., plants, conditions, etc.).

[0029] In one particular example, the management system 102 may utilize the cartridge data 108 along with the sensor data 104 provided by the device 106 to track expected germination rates or other performance metrics associated with individual cartridges. For example, as described above, a supplier, grower, and / or manufacturer may provide or agree to an expected germination or harvest rate when engaged to provide cartridges to the device user 114 on behalf of the management system 102. In this example, the management system 102 may determine the actual germination or harvest rate of the cartridges manufactured by the supplier (e.g., grower, cartridge manufacturer, assembler, seed source, combinations thereof, etc.). The management system 102 may then determine whether the supplier met or exceeded the expected germination and / or harvest rate. If the supplier did not meet or exceed the expected germination and / or harvest rate, the management system 102 may alert the supplier, the user, and / or another responsible party via the third party system 110, via the supplier system 120. In some cases, the management system 102 may also adjust cartridge order rates based on actual germination and / or harvest rates determined from the sensor data 104 and / or cartridge data 108.

[0030] In some cases, based on the policies and parameters 114 and the user data 112, the management system 102 may be configured to place orders 118 on behalf of or for the user 114 associated with different devices 106 at one or more supplier systems 120. For example, if the user 114 appears to prefer one supplier over another or one type of plant over another (e.g., one type of lettuce over another type of lettuce), the management system 102 may update or change the supplier and select the supplier that the user 114 is determined to prefer.

[0031] In some particular examples, the management system 102 may determine from the sensor data 104 from the device 106 a consumption rate or harvest rate of the plants in the device 106. The management system 102 may then adjust periodic orders (weekly orders, monthly orders, quarterly orders, etc.) based on the harvest rate. In some cases, based on the type of plants harvested, the management system 102 may adjust order amounts, plant mixes, etc. For example, if a user appears to prefer kale over spinach, the system 102 may increase kale cartridge orders while similarly decreasing orders for spinach cartridges. In some cases, the management system 102 may also order new types of plants based on similar flavor profiles and / or consumption or harvest patterns (over a period of time such as the previous week, month, quarter, etc.). In this way, the management system 102 may present each user with additional plants with different nutritional and taste profiles that are more likely to be enjoyed by the user than other random selections or suggestions of new plants.

[0032] In some cases, the management system 102 may determine policies that require, at least in part, user action. For example, the management system 102 may determine optimal cartridge placement within the apparatus 106 for each type of plant. In these cases, the user 114 may need to place or insert the cartridge accordingly. In these cases, the management system 102 may also generate user instructions 134 to instruct the user, for example, via a downloadable application hosted on the user electronic device, to insert a particular cartridge into a particular location within the apparatus 106.

[0033] In some examples, the management system 102 may generate reports 122 that include device metrics associated with individual devices, such as device 106, for example, cartridge harvest rate or germination rate, among other aggregated data, such as aggregated user data 112. In the illustrated example, the reports 122 may be provided to the supplier system 120 and the third party system 110.

[0034] In some cases, the management system 102 may also track multiple cartridges for each device 106. In addition to determining the supplier, manufacturer, and / or grower, the management system 102 may determine the total number and / or type of cartridges for each cartridge in each device. In some cases, the total number and / or type of cartridges may be included in the report 122.

[0035] In the present example, sensor data 104, cartridge data 108, third party data 130, orders 118, user instructions 134, and / or reports 122 may be transmitted and / or received by management system 102 over various networks, such as networks 124-130.

[0036] 2 is an example block diagram of a management system architecture 200, such as the management system 102 of FIG. 1, for determining parameters of plants associated with an enclosed growing environment or apparatus. In the current example, the management system 102 may be configured to receive user data from a user interface 224 (e.g., a web-based application and / or a downloadable application) at a gateway system 202. The user data may be stored, at least in part, as system data 204. In some cases, the system data 204 may also include third party data received from one or more third party systems 206, cartridge data 208 received from various systems in communication with or in proximity to the actual seed cartridges, sales data associated with one or more sales, business, CRM, ERP, or reporting systems 210.

[0037] The management system 102 may also receive sensor data 212 from one or more devices, such as the device 106, via the gateway 202. As discussed above, the sensor data may include temperature data, image data, air quality data, light data, water data, etc. associated with the device 106. In the present example, the sensor data 212 may be processed by a sensor data processing system 214 or a computer vision system / engine. In this example, the sensor data processing system 214 may segment, classify, or otherwise extract data, features, characteristics, etc. from the sensor data 212.

[0038] The extracted data may then be processed along with the system data 204 by the decision system 216. The decision system 216 may also access the data store housing the configuration data 218. In this example, the decision system 216 may update the configuration data 218 based on the system data 204, the extracted data, and one or more machine learning models or networks. For example, the decision system 216 may apply a multi-arm bandit technique to the received data to help update the configuration data 218 to meet the user's preference data requirements and improve the overall yield, production, and quality of the plants grown on the apparatus 106.

[0039] The configuration system 220 may provide the updated policies 222 to the devices 106 via a push notification service, as shown. In some cases, the updated policies 222 may be global, regional, as a set of similar users, per device 106, per plant or receptacle within each device 106, and / or combinations thereof. Thus, in some cases, the updated policies 222 may be customized for individual users and devices 106, while in other cases, the updated policies 222 may be across a set, multiple related sets, or even all networked devices 106.

[0040] In some cases, configuration system 220 may utilize one or more machine learning models or networks to determine configuration updates 222. For example, configuration system 220 may store a mirror copy of the settings and state of each device 106 (e.g., the state of individual plants, the internal and external environments of the devices 106, etc.). The mirror copy and any suggested or proposed updates by decision system 216 may be input into the machine learning models and / or networks, and configuration system 220 may receive configuration updates 222 as an output, as described below with respect to FIG.

[0041] The devices 106 may be organized into groups based on plant variety, geographic location, user preferences, third party systems 110, or any other configuration of 102 and sensor data. Groupings may receive configuration updates 222 from the management system 102, be updated automatically from the decision engine, be updated manually from a business reporting system, or not maintain with default control parameters updated. Custom groupings may be created manually or automatically by components of the system 102 (such as business applications) and assigned to a grouping or multiple groupings. Software / firmware updates may be pushed to target groups. New cartridges / plant varieties may be provided to target groups.

[0042] In some cases, the grouping may have multiple layers. A first layer may include whether the device 106 is part of a manual configuration or part of an automatic configuration. In this case, the automatic configuration may utilize a default configuration and / or any generated configuration updates 222. The system 102 may also have a second layer that may include segmentation groupings. The segmentation groupings may be based on geographic regions, environmental conditions, plant growth stages, plant types, similarity of user preferences, and the like. For example, the second layer may include grouping sets of devices 106 to have shared configuration updates 222 based on the various segmentations described above. In some cases, the system 102 may change or update the groupings, particularly the second layer groupings, periodically or in response to detected changes (such as harvest or cartridge insertion events).

[0043] 3 is an example block diagram of an architecture 300 associated with a management system, such as management system 102, for determining parameters of plants associated with an enclosed growing environment or apparatus 106. In the current example, the management system may receive user input via user interface 224 associated with one or more criteria, generally indicated by 302. The criteria 302 may include user preferences associated with, for example, water, algae, harvest, tissue metrics, size, nutrients, water level set points, water valve open time, water valve open frequency, pump frequency, pump on time, grow light on time, grow light intensity, temperature set points, tower rotation speed, tower rotation time, UV light on time, device telemetry upload / download frequency, etc. The criteria 302 may be processed by a landing zone 312 that is output to a queue 314 and an indexer 310, as shown.

[0044] The management system also receives data from one or more sensors 316 via stream 304 at a stream ingest 306. The output of the stream ingest 306 may be stored in a data repository 308. The data repository 308 may also store the output of the indexer 310. In the present example, the data repository 308 is accessible and updatable by a decision system 216, which may utilize the data stored in the data repository 308 to update the configuration data 218. The configuration system 220 may then generate policies and parameters and output the policies and parameters to one or more devices 106, as described above.

[0045] 4 is an example block diagram of an architecture 400 associated with a management system for determining parameters 116 of an enclosed growing environment or apparatus 106. In some cases, the management system may monitor and adjust parameters 116 associated with one or more growing apparatuses 106 to optimize or improve harvest rates, growing conditions, and energy / water consumption of the apparatuses 106 as conditions and plants within the enclosed growing environment of the apparatuses 106 are adjusted, harvested, and otherwise changed.

[0046] In the present example, the management system may include an application programming interface 402 configured to receive sensor data 104 from the growth apparatus 106 via a communication interface 404, such as an Internet of Things (IOT) enabled core, processor, and / or antenna. The API system 402 may also be configured to receive user data 112 from a user interface 224, such as a web or app accessible user interface 224. As described above, the sensor data 104 may include temperature data, image data, light data, etc. associated with the apparatus 106. In some cases, the sensor data 104 may also include water data, such as incoming water supply quality data, sequestered water data (e.g., water that has been sequestered by the apparatus 106, e.g., to remove heavy metals, etc., before being introduced into the recirculating water supply), and distributed or recirculating water data. The sensor data 104 may also include air quality data, which may include multiple stages of air, such as incoming air supply quality data, sequestered air data (e.g., air that has been sequestered by the apparatus 106 before being introduced into the apparatus's air supply), and distributed or recirculating air data.

[0047] User data 112 may include settings from a user device associated with the user, such as user preferences (e.g., plant flavor, plant color, leaf size at time of consumption, plant age or life cycle at time of consumption, etc.), desired plant size, desired plant type (species or family), favorite recipes, favorite seasonings, cooking or preparation style, food pairings, etc. User data 112 may include data from third party applications associated with the user 114 (e.g., social media applications, marketplace applications, smart home applications, etc. that the user has authorized to access and / or communicate with the management system).

[0048] The parameters 116 may include lighting settings, humidity settings, temperature settings, watering settings, etc. In some cases, the parameters 116 are set for each individual receptacle and plant combination within the growing device 106 to tailor plant growth for an individual user based on the user data 112.

[0049] In the current example, the API system 402 may store the sensor data 104 and the user data 112 in a data repository 308. The data repository 308 may be accessible by a configuration engine 408 (including, e.g., the determination system 216, the configuration system 220, etc. from FIG. 3). The configuration engine 408 may be configured to determine parameters 116 for each receptacle of the device 106 based at least in part on the pantry or cartridge associated with the receptacle, the user data 112, and / or the sensor data 104. In the current example, the configuration engine 408 may provide at least a portion of the user data 112, the sensor data 104, and other data (criteria, cartridge data, aggregate data, etc.) to one or more machine learning systems 406 (e.g., one or more machine learning models and / or networks). The configuration engine 408 may then receive the parameters 116 and / or additional data usable to determine the parameters 116 as an output of the machine learning system 406. In some cases, the machine learning models and / or networks of the machine learning system 406 may be trained using sensor data 104, user data 112, cartridge data, third party data, and / or other data associated with one or more devices over a prior period of time.

[0050] 5-7 are flow diagrams illustrating exemplary processes associated with the management systems discussed herein. The processes are illustrated as a collection of blocks in a logical flow diagram, which represents a sequence of operations, some or all of which may be implemented in hardware, software, or a combination thereof. In the context of software, the blocks represent computer-executable instructions stored on one or more computer-readable media that, when executed by one or more processors, perform the described operations. Generally, computer-executable instructions include routines, programs, objects, components, encryption, decryption, compression, recording, data structures, etc. that perform particular functions or implement particular abstract data types.

[0051] The order in which the operations are described should not be construed as a limitation. Any number of the described blocks can be combined in any order and / or in parallel to implement a process or alternative processes, and not all blocks need to be executed. For purposes of explanation, the processes herein are described with reference to the frameworks, architectures, and environments described in the examples herein, although the processes may be implemented in a wide variety of other frameworks, architectures, or environments.

[0052] 5 is an exemplary flow chart illustrating an exemplary process for updating policies or configurations associated with a management system, according to some implementations. As described above, the management system can be configured to adjust parameters of an indoor growing apparatus having an enclosed growing environment. In some cases, the management system is a cloud-based service that utilizes data aggregated across multiple growing apparatuses, as well as personal data from each user and each apparatus, to generate customized parameters for each individual plant or receptacle (e.g., growing area) within the enclosed growing environment.

[0053] At 502, the management system may receive sensor data associated with one or more devices. As discussed herein, the sensor data may include temperature data, image data, light data, etc. associated with the device. In some cases, the sensor data may also include water data, such as incoming water supply quality data, sequestered water data (e.g., water that has been sequestered by the device 106, e.g., to remove heavy metals, etc., before being introduced into the recirculating water supply), and dispensed or recirculating water data. The sensor data may also include air quality data, which may include multiple stages of air, such as incoming air supply quality data, sequestered air data (e.g., air that has been sequestered by the device 106 before being introduced into the device's air supply), and dispensed or recirculating air data. In some cases, the sensor data may also include data associated with the environment surrounding or external to the device.

[0054] At 504, the management system may receive cartridge data associated with one or more cartridges inserted into one or more devices. Cartridge data may be received from devices as well as third party systems (such as supplier systems, manufacturer systems, transport systems, and / or other processing systems). For example, cartridge data may include plant species, family, expected germination rate, country of origin, growing or cartridge packaging facility, seed insertion and / or packaging date and / or timestamp, manufacturer demographic data, polymer demographics and manufacturing origin and date, seed insertion and / or packaging location, seed insertion and / or packaging conditions, planting location, harvest time and location, expected germination rate, expected germination time, expected growth time, historical germination or growth time per plant species, etc. Cartridge data may also include fertilizer supplier formulations and concentration amounts, growing media material properties, intended consumer demographic information, distributor demographic data, material properties, expected material degradation rate, material degradation location, nutrient deficiencies, pests, and / or plant disease tracking, cartridge response tracking to water chemistry (e.g., improper water chemistry, changes in water chemistry, etc.). Some cartridge data may be assigned to a particular cartridge identifier for variables such as consumer response to botanical taste, nutrition, texture, color, etc.

[0055] At 506, the management system may receive user data and third party data associated with one or more users of the one or more devices via a user interface. User data may include settings from a user device associated with the user, such as user preferences (e.g., plant flavor, plant color, leaf size at time of consumption, plant age or life cycle at time of consumption, etc.), desired plant size, desired plant type (species or family), favorite recipes, favorite seasonings, cooking or preparation style, food pairings, etc. Third party data may include data from third party applications (e.g., social media applications, marketplace applications, smart home applications, etc.) associated with the user or other users who grow similar combinations of plants that have authorized the user to access and / or communicate with the management system.

[0056] At 508, the management system may determine one or more characteristics of the plants associated with the one or more devices based at least in part on the sensor data. For example, the management system may utilize one or more machine learning models to determine characteristics such as health, size, quality, color, variety, germination, growth stage, growth rate, etc.

[0057] At 510, the management system may determine a configuration update based at least in part on one or more characteristics, cartridge data, user data, and / or third-party data. For example, the system may determine whether a user's preferences match the expected production of the device for one or more cartridges. As another example, the management system may determine whether the estimated nutritional value of the plant is within a desired range as indicated by third-party data (e.g., user health data).

[0058] At 512, the management system may control at least one setting of the at least one device based at least in part on the configuration update. For example, the management system may push the configuration update to a selected device or group of devices, causing them to change one or more growth conditions based on the configuration update, as described herein.

[0059] 6 is an exemplary flow chart illustrating an exemplary process for updating ordering instructions associated with a management system, according to some implementations. As described above, the management system may be configured to track the performance of suppliers, manufacturers, and / or growers associated with cartridges made available for the growing device. In some cases, the management system may be a cloud-based service that utilizes data aggregated across multiple growing devices, as well as personal data from each user and each device, to help evaluate the quality of cartridges produced by different suppliers, manufacturers, growers, and / or combinations thereof.

[0060] At 602, the management system may receive sensor data associated with one or more devices. As discussed herein, the sensor data may include temperature data, image data, light data, etc. associated with the device. In some cases, the sensor data may also include water data, such as incoming water supply quality data, sequestered water data (e.g., water that has been sequestered by the device before being introduced into the recirculating water supply, e.g., to remove heavy metals, etc.), and distributed or recirculating water data. The sensor data may also include air quality data, which may include multiple stages of air, such as incoming air supply quality data, sequestered air data (e.g., air that has been sequestered by the device before being introduced into the device's air supply), and distributed or recirculating air data. In some cases, the sensor data may also include data associated with the environment surrounding or external to the device.

[0061] At 604, the management system may receive third-party data associated with one or more devices. The third-party data may be received not only from the devices, but also from third-party systems (such as social media, marketplaces, universities, healthcare providers, supplier systems, manufacturer systems, transportation systems, and / or other systems).

[0062] At 606, the management system may receive user data associated with one or more users of the one or more devices via a user interface. The user data may include settings from a user device associated with the user, such as user preferences (e.g., plant flavor, plant color, leaf size at time of consumption, plant age or life cycle at time of consumption, etc.), desired plant size, desired plant type (species or family), favorite recipes, favorite seasonings, cooking or preparation style, food pairings, etc.

[0063] At 608, the management system may determine one or more characteristics of the plants associated with the one or more devices based at least in part on the sensor data. For example, the management system may utilize one or more machine learning models to determine characteristics such as health, size, quality, color, variety, germination, growth stage, growth rate, pest infestation, etc. In some cases, the management system may segment and / or classify image data associated with the sensor data received from the one or more devices to identify individual areas, plants, or features within the growing environment and assign identifiers (plant species, parts, etc.).

[0064] At 610, the management system may determine a performance metric (such as germination rate or harvest rate) associated with at least one device (or individual cartridge) based at least in part on the one or more features, third-party data, and / or user data. For example, the system may use the third-party data and one or more features and one or more machine learning models to determine the quality of the plants associated with a particular device. In some cases, the system may determine the performance of a supplier, manufacturer, and / or grower based on the quality of the plants associated with a cartridge prepared and / or shipped by the manufacturer. For example, the sensor data may include image data that allows the management system to determine the supplier, manufacturer, and / or grower (e.g., via markings on the cartridge or cartridge lid). The system may then associate the performance of the cartridge to a supplier, manufacturer, and / or grower based on, for example, the aggregate performance of the cartridges associated with each supplier, manufacturer, and / or grower in the device.

[0065] In some cases, the system may also associate specific policies or parameters with cartridges produced by individual suppliers, manufacturers, and / or growers in a manner similar to plant species, subspecies, genera, plant varieties, varieties, etc.

[0066] The management system may then update orders (e.g., quantities) associated with facilities (e.g., suppliers, manufacturers, and / or growers) based at least in part on the performance metrics at 612. For example, the management system may reduce order quantities if a harvest rate, health metric, or quality metric of cartridges associated with the facility falls below a threshold.

[0067] 7 is an exemplary flow chart illustrating an exemplary process for updating parameters associated with a management system in some implementations. As described above, the management system may be configured to track the performance of suppliers, manufacturers, and / or growers associated with cartridges made available for a growing device. In some cases, the management system is a cloud-based service that utilizes data aggregated across multiple growing devices, as well as personal data from each user and each device, and may generate customized parameters for each individual plant or receptacle (e.g., growing area) within the closed growing environment based on the individual supplier, manufacturer, and / or grower who produced each cartridge.

[0068] At 702, the management system may receive sensor data associated with one or more devices over a period of time. As discussed herein, the sensor data may include temperature data, image data, light data, etc. associated with the device. In some cases, the sensor data may also include water data, such as incoming water supply quality data, sequestered water data (e.g., water that has been sequestered by the device before being introduced into the recirculating water supply, e.g., to remove heavy metals, etc.), and distributed or recirculating water data. The sensor data may also include air quality data, which may include multiple stages of air, such as incoming air supply quality data, sequestered air data (e.g., air that has been sequestered by the device before being introduced into the device's air supply), and distributed or recirculating air data. In some cases, the sensor data may also include data associated with an environment surrounding or external to the device. In some cases, the period of time may be a period of time associated with planting, growing, and / or harvesting of plants for the growing device. In other cases, the period of time may be a predetermined period of time, such as a week, month, quarter, etc.

[0069] At 704, the management system may receive cartridge data from one or more manufacturer and / or supplier systems. The cartridge data may include plant species, family, expected germination rate, growing facility, seed insertion and / or packaging date and / or timestamp, manufacturer demographic data, seed insertion and / or packaging location, seed insertion and / or packaging conditions, expected germination rate, expected germination time, expected growth time, historical germination or growth time for each plant species, etc.

[0070] At 706, the management system may determine, based at least in part on the cartridge data and the sensor data, that the first cartridge is associated with the first device, is of a first type, and is manufactured by a first supplier, manufacturer, and / or grower. For example, the system may utilize image data received as part of the sensor data to identify a particular cartridge associated with a particular supplier, manufacturer, grower, and / or combination thereof. In other cases, the user may provide an identifier associated with a supplier, manufacturer, grower, and / or combination thereof via a user interface, as described above. The user may also provide a location or receptacle into which the cartridge was inserted to assist the management system in determining that the first cartridge is associated with the first device, is of a first type, and is manufactured by a first supplier, manufacturer, and / or grower.

[0071] At 708, the management system may determine, based at least in part on the cartridge data and the sensor data, that the second cartridge is associated with the second device, is of a first type, and is manufactured by a first supplier, manufacturer, and / or grower. For example, the system may again utilize image data received as part of the sensor data from the second device to identify a particular cartridge associated with a particular supplier, manufacturer, grower, and / or combination thereof. In other cases, the user may provide an identifier associated with a supplier, manufacturer, grower, and / or combination thereof via a user interface, as described above. The user may also provide a location or receptacle into which the cartridge was inserted to assist the management system in determining that the first cartridge is associated with the first device, is of a first type, and is manufactured by a first supplier, manufacturer, and / or grower.

[0072] At 710, the management system may determine parameters or configurations associated with the first supplier, manufacturer, and / or grower based at least in part on the sensor data associated with the first cartridge and the second cartridge over a period of time. For example, the system may determine that the first cartridge produced higher quality plants than the second cartridge. The system may then determine parameter differences between the first and second devices and associated with the first and second cartridges. The system may then determine parameter adjustments based at least in part on the differences. In some cases, the management system may test the new parameters on additional devices with cartridges having the same type, supplier, manufacturer, grower, etc. to determine consistency of results when the new parameters are applied.

[0073] At 712, the system may update other devices associated with the first supplier, manufacturer, and / or grower having a type of inserted cartridge based at least in part on the parameters or configuration. For example, upon detecting seed cartridges associated with the supplier, manufacturer, and / or grower and having the same type as the first and second cartridges in the additional devices, the management system may apply the configuration and / or parameters that resulted in a higher quality harvest.

[0074] FIG. 8 is an exemplary diagram of cloud-based services associated with the management system 102, according to some implementations. The management system 102 may include one or more communication interfaces 802 (also referred to as communication devices and / or modems). The one or more communication interfaces 802 may enable communication between the management system 102 and one or more other local or remote computing devices or remote services. For example, the communication interfaces 802 may facilitate communication with other sensor systems, devices, user interfaces, and / or other third party systems. The communication interfaces 802 may enable Wi-Fi-based communication, such as over frequencies defined by the IEEE 802.11 standard, short-range wireless frequencies such as Bluetooth, cellular communication (e.g., 2G, 3G, 4G, 4G LTE, 5G, etc.), satellite communication, dedicated short-range communications (DSRC), Ethernet, or any suitable wired or wireless communication protocol that enables each computing device to interface with other computing devices.

[0075] The management system 102 may include one or more processors 804 and one or more computer-readable media 806. Each processor 804 may itself include one or more processors or processing cores. The computer-readable media 806 is illustrated to include memory / storage. The computer-readable media 806 may include volatile media (such as random access memory (RAM)) and / or non-volatile media (such as read-only memory (ROM), flash memory, optical disks, magnetic disks, etc.). The computer-readable media 806 may include fixed media (e.g., GPU, NPU, RAM, ROM, fixed hard drives, etc.) as well as removable media (e.g., flash memory, removable hard drives, optical disks, etc.). The computer-readable media 806 may be configured in a variety of other ways, as further described below.

[0076] A number of modules, such as instructions, data stores, etc., may be stored in the computer readable medium 806 and configured to execute on the processor 804. For example, as shown, the computer readable medium 806 stores data extraction instructions 808, ordering instructions 810, decision engine instructions 812, parameter determination instructions 814, alert instructions 816, model training instructions 818, and other instructions 820, such as an operating system. The computer readable medium 806 may also be configured to store data, such as sensor data 822, user data 824, cartridge data 826, machine learning models 828, environmental data 830, and / or third party data 632, and other types of data.

[0077] The data extraction instructions 808 may be configured to determine characteristics associated with the growing device, the inserted cartridge, or the developing plant. In some cases, the data extraction instructions 808 may utilize one or more machine learning models and / or networks to analyze, segment, and / or classify data, such as image data captured regarding the interior of the growing device. For example, the data extraction instructions 808 may determine the area of ​​the planting column, a plant identifier, a cartridge identifier, plant condition (e.g., size, life stage, health, etc.), etc.

[0078] The order instructions 810 may be configured to adjust order quantities from third party suppliers (such as manufacturers and growers) of cartridges. For example, the system may adjust cartridge orders based on detected or determined germination rates, plant quality, plant health, plant yield, etc.

[0079] The decision engine instructions 812 may also access the configuration data in the data store housing to update the configuration data based on the received or stored data (e.g., user data, sensor data, third party data, cartridge data, etc.) and one or more machine learning models or networks. For example, the decision engine instructions 812 may apply a multi-arm bandit technique to the received data to update the configuration data to help improve the overall yield, production, and quality of plants grown with the apparatus to meet the user's preference data requirements.

[0080] The parameter determination instructions 814 may be configured to determine parameters to improve overall yield, production, and quality of plants grown with the apparatus to meet user preference data requirements based on user data, sensor data, third party data, and cartridge data associated with multiple users and the apparatus. In some cases, the data may be aggregated prior to determining the parameters. In some cases, the parameter determination instructions 814 may select and apply new parameters and / or policies to different apparatus to see improved quality, yield, and / or production consistency of plants based on application of the parameters, policies, and / or configurations.

[0081] The alert instructions 816 may be configured to provide an alert to a user interface associated with a plant, a cartridge, an apparatus, or the like. For example, the alert instructions 816 may include an alert to a user to harvest a particular plant within the apparatus. As another example, the alert instructions 816 may send an alert to a user interface to notify the user of a change in a parameter, policy, or the like, associated with the user's apparatus. In some cases, the alert instructions 816 may handle a user response to the alert, such as confirmation or approval of the parameter, policy, and / or configuration change, as well as a rejection.

[0082] The model training instructions 818 may be configured to train the machine learning model 828 based on the training data and / or user input.

[0083] Although the subject matter has been described in language specific to structural features, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the particular features described. Rather, the particular features are disclosed as exemplary forms of implementing the claims.

Claims

1. Receiving first sensor data from a first sensor of a first system, wherein the first sensor data represents a first area associated with a first housing of the first system, and the first housing is configured to provide a first controlled physical environment; the receiving step; Determining a first characteristic associated with a first plant inhabiting the first area, at least partially based on the first sensor data; Determining at least one first setting for the first system, at least partially based on the first characteristic; Causing the first system to apply the at least one first setting to the first area; A method comprising the above steps.

2. Receiving second sensor data from a second sensor of a second system, wherein the second sensor data represents a second area associated with a second housing of the second system, and the second housing is configured to provide a second controlled physical environment; the receiving step; Determining a second characteristic associated with a second plant inhabiting the second area, at least partially based on the second sensor data; and further comprising The step of determining the at least one first setting of the first system is at least partially based on the second characteristic. The method according to claim 1.

3. Receiving first cartridge data from a first third-party system, wherein the first cartridge data represents data and an identification of a first seed cartridge; the receiving step; Determining that the first seed cartridge is associated with the first area, at least partially based on the first sensor data and the first cartridge data; Determining a first metric associated with the first third-party system, at least partially based on the first sensor data; Adjusting an order for an additional seed cartridge using the first third-party system, at least partially based on the first metric. The method according to claim 1, further comprising the above steps. Step of receiving second cartridge data from a first third-party system, wherein the second cartridge data represents data and an identification of a second seed cartridge, and the step of receiving; Determining that the second seed cartridge is associated with the second region, based at least in part on the second sensor data and the second cartridge data; Determining a second metric associated with the first third-party system, based at least in part on the second sensor data; Adjusting an order for additional seed cartridges using the first third-party system, based at least in part on the second metric, the method of claim 2 further comprising.

5. Further comprising the step of receiving first third-party data from a second third-party system, Determining the at least one first setting of the first system is based at least in part on the first third-party data, the first cartridge data, and the first feature, the method of claim 3.

6. The first feature is The health of the first plant, The life stage of the first plant, The size of the first plant, The classification or type of the first plant, The method of claim 1, including one or more of.

7. The first system includes a planting column configured to rotate about a vertical axis within the first housing, The first region is associated with a receptacle of the planting column, the method of claim 1.

8. The step of determining the at least one first setting of the first system is Inputting the first sensor data into one or more machine learning models or networks, Receiving the at least one first setting as an output from the one or more machine learning models or networks, the method of claim 1 further comprising.

9. Further comprising the step of receiving user data from a user device, Determining the at least one first setting of the first system is based at least in part on the user data and the first feature, the method of claim 1.

10. A system comprising: one or more processors; one or more non-transitory computer-readable media storing instructions executable by the one or more processors, which when executed cause the system to: receive first sensor data from a first sensor of a first system, the first sensor data representing a first region associated with a first housing of the first system, the first housing being configured to provide a first controlled physical environment; receive second sensor data from a second sensor of a second system, the second sensor data representing a second region associated with a second housing of the second system, the second housing being configured to provide a second controlled physical environment; determine at least one parameter of the first system based at least in part on the first sensor data and the second sensor data; send the at least one parameter to the first system; and perform operations including these. **Claim 11** The operations further include: receiving user data from a user device; receiving first third-party data from a first third-party system; and the determining of the at least one parameter of the first system is based at least in part on the user data and the first third-party data. The system according to claim 10. **Claim 12** The operations include: receiving first cartridge data from a first third-party system, the first cartridge data representing data and an identification of a first seed cartridge; determining that the first seed cartridge is associated with the first third-party system based at least in part on the first sensor data and the first cartridge data; determining a first metric associated with the first third-party system based at least in part on the first sensor data; Further including adjusting an order for an additional seed cartridge using the first third - party system, at least partially based on the first metric, the system according to claim 10.

13. The operation is Receiving second cartridge data from the first third - party system, wherein the second cartridge data represents data and an identification of a second seed cartridge, the receiving Determining that the second seed cartridge is associated with the second region, at least partially based on the second sensor data and the second cartridge data Determining a second metric associated with the first third - party system, at least partially based on the second sensor data Further including adjusting the order for the additional seed cartridge using the first third - party system, at least partially based on the second metric, the system according to claim 12.

14. The operation is Further including determining a first characteristic associated with a first plant inhabiting the first region, at least partially based on the first sensor data, and Determining the at least one parameter of the first system is at least partially based on the first characteristic, the system according to claim 10.

15. Determining a first characteristic associated with a first plant inhabiting the first region, at least partially based on the first sensor data, is Inputting the first sensor data into one or more machine - learning models or networks, and Receiving the first characteristic as an output from the one or more machine - learning models or networks, the system according to claim 14.

16. A gateway system that receives at least first sensor data from a first growth device, receives second sensor data from a second growth device, and transmits a configuration update to the first growth device and the second growth device, A sensor data processing system that segments or classifies the first sensor data and the second sensor data, A determination system that determines the configuration update, at least partially based on an output of the sensor data processing system, A system comprising.

17. The gateway system is further configured to receive seed cartridge data and user data, The determination system is further configured to determine the configuration update based at least in part on the seed cartridge data and the user data, the system according to claim 16.

18. The user data includes at least one criterion of a plant associated with the first growth device or the second growth device, the system according to claim 17.

19. The at least one criterion is a preference for water, a preference for environment, a preference for lighting, a preference for algae, a preference for harvest, a preference for tissue metric, a preference for size, or a preference for nutrient metric, including at least one of the above, the system according to claim 18.

20. The sensor data processing system is further configured to determine third party data associated with manufacturing a seed cartridge of a plant housed within the first growth device or the second growth device based at least in part on the seed cartridge data and the first sensor data or the second sensor data, the system according to claim 17.

21. The system adjusts an order associated with the third party data in response to determining that the third party data is associated with manufacturing the seed cartridge of the plant housed within the first growth device or the second growth device, the system according to claim 20.

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