System for monitoring enclosed habitats

Abstract

A system associated with an enclosure (100) for providing a controlled environment for the cultivation of plants, produce, and the like. The system may be configured to provide customized lighting for individual plants based on the plant's species, size, health, and / or life stage. In some cases, the system may include multiple sensors for capturing data (118) associated with the enclosure (100) to identify the plant and provide customized lighting.

Description

[Technical Field]

[0001] The present invention relates to a system for monitoring enclosed habitats. [Background technology]

[0002] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application claims priority to U.S. Provisional Application No. 63 / 199,838, entitled "SYSTEM FOR MONITORING ENCLOSED GROWING ENVIRONMENT," filed January 28, 2021, which is incorporated herein by reference in its entirety.

[0003] In response to food deserts, where access to fresh produce is limited in densely populated areas, home gardening and the use of microgardens in apartment complexes and neighborhoods have increased in recent years across the United States. More consumers want to grow fresh produce and herbs at home to provide fresher produce and 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 and provide unitary growing conditions for all the produce and herbs being grown by the homeowner, which often results in suboptimal conditions for all the produce and herbs being grown by the homeowner. Additionally, homeowners often lack the education and time to properly maintain optimal growth conditions for each individual species and type of plant. [Brief explanation of the drawings]

[0004] The detailed description will now be described 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. Use of the same reference number in different figures indicates similar or identical components or features. [Figure 1]FIG. 1 is a diagram illustrating an example of cloud-based services associated with an enclosure, according to some implementations. [Figure 2] FIG. 1 is a perspective view illustrating an example of the exterior of an enclosure for providing a controlled growing environment, according to some implementations. [Figure 3] 2 is a perspective view illustrating an example of the interior of the housing in FIG. 1 according to some embodiments. [Figure 4] 3 is a perspective view illustrating another example of the housing in FIGS. 1 and 2 according to some implementations. FIG. [Figure 5] 3 is a front view illustrating an example of the housing in FIGS. 1 and 2 according to some implementations. [Figure 6] 10A-10C are front views illustrating examples of planting columns associated with the housing, according to some embodiments. [Figure 7] 1 is an exploded view illustrating an example of a planting post of an enclosure, according to some embodiments. [Figure 8] 3 is a pictorial view illustrating an example front view of a plant pole and a lighting and control column associated with the housing in FIGS. 1 and 2, according to some implementations. [Figure 9] 3 is a pictorial diagram illustrating an example of a top view of a plant pole and a light and control pole associated with the housing in FIGS. 1 and 2, according to some implementations. [Figure 10] 2 is a perspective view illustrating an example seed cartridge for use with a planting post associated with the housing in FIG. 1 according to some embodiments. FIG. [Figure 11] 1 is a perspective view illustrating an example of a seed cartridge engaged with a planting receptacle of a planting pole associated with the housing in FIG. 1, according to some embodiments. [Figure 12]1 is a perspective view illustrating an example of a seed cartridge being inserted into a planting container of a planting post, according to some embodiments. FIG. [Figure 13] FIG. 1 is a flow diagram showing an example of an illustrated process for determining settings for lighting and control systems associated with an individual plant, according to some implementations. [Figure 14] FIG. 10 is a flow diagram illustrating another example of an illustrated process for determining characteristics of an individual plant, according to some implementations. [Figure 15] FIG. 10 is a flow diagram illustrating another example of an illustrated process for determining characteristics of an individual plant, according to some implementations. [Figure 16] FIG. 10 is a flow diagram illustrating another example of an illustrated process for eliciting a shade avoidance response from individual plants, according to some implementations. [Figure 17] 2 is a diagram illustrating an example of a control system associated with the housing in FIG. 1, according to some embodiments.

[0005] The drawings depict various embodiments for purposes of illustration only, and those skilled in the art will readily appreciate 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 INVENTION

[0006] [Detailed explanation] Described herein are systems and apparatuses for automated and assisted monitoring and environmental control in home gardens and microgardens. For example, the systems described herein may be configured to provide an enclosed growing environment for home and indoor cultivation of plants and fungi, flowers, produce, mushrooms, and / or herbs. In some implementations, the systems may provide an isolated enclosure configured to provide stable and controlled environmental conditions physically separated from conditions within the surrounding environment (e.g., a home or apartment). However, unlike traditional home garden systems that provide uniform lighting and temperature, the enclosures described herein may provide active monitoring and adaptive environmental conditions based on health, stage of growth, plant type or species, etc.

[0007] In some particular implementations, the system may be configured to monitor individual plants within the growing environment and provide tailored growing conditions, such as custom lighting (e.g., tower rotation, tilt, and / or angular position / orientation, focal length, temperature, specific wavelength, intensity, and amount, etc.). In some cases, the individual growing conditions may be based on the type or species of the individual plants as well as the detected or determined health, size, and / or stage of growth or reproduction of the individual plants within the enclosure. Additionally, the system may also be used to induce post-harvesting drying conditions at the end of the plant's growth cycle.

[0008] In one embodiment, the system may include a planting column or tower within the housing. The planting column may include a single or multiple receptacles configured to receive individual plants. The planting receptacles may be arranged in both vertical columns and horizontal rows 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 rows, with each column having one planting receptacle for every other row. In these cases, staggering the planting receptacles not only allows the system to monitor individual plants, but also ensures that the individual plants have sufficient room to grow.

[0009] In some cases, the planting post may be rotatable partially or completely 360 degrees around a base within the housing, or may be capable of any other limited rotation. For example, a drive motor may be configured to mechanically or magnetically rotate the planting post within the housing based on one or more control signals from a monitoring and control system. In some cases, each individual planting container may be assigned a unique identifier so that as the planting post rotates, the system can track each plant based on its determined position within the planting post. In these cases, the system may determine the assigned position of a plant when inserting or planting a plant into a particular planting container. For example, the planting container may have visible or invisible markings (e.g., marks in the infrared spectrum, etc.) that the system may read upon insertion of a planting pod. In other cases, the system may determine that a container is full as the planting post rotates. In some cases, location markings may be placed at various locations on the interior surface of the housing and / or around the top and bottom of the planting pole to assist in initialization or location upon restarting or rebooting the system and as updated or replacement lighting and control poles are installed or calibrated.

[0010] In some implementations, lighting and control poles or panels may be configured within the enclosure or along specific areas of the enclosure. The lighting and control poles may be equipped with various sensors for monitoring individual plants. For example, the lighting and control poles may be equipped with one or more sensors, such as imaging devices (e.g., RGB (red-green-blue) imaging devices, infrared image devices, monochrome image devices, and lidar devices), humidity sensors, temperature sensors, carbon dioxide (CO2) sensors, and spectral sensors. The lighting and control poles may be equipped with one or more illuminators (e.g., visible lights, infrared illuminators, ultraviolet lights, lasers, and projectors). The illuminators may be adjustable to provide specific spectra, light amounts, and light intensities to individual planting containers based on the health, life stage, size, and type or species of the corresponding plant.

[0011] In some cases, a lighting and control post may include multiple rows of sensors and / or illuminators. For example, a lighting control post may include a top row of sensors and / or illuminators, a middle row of sensors and / or illuminators, and a bottom row of sensors and / or illuminators. In other cases, a lighting and control post may include a row of sensors and / or illuminators for each corresponding row of planting posts. In some implementations, the field of view or region of interest associated with each sensor and / or illuminator may be adjustable so that a single sensor and / or illuminator may each capture data and provide light to multiple planting containers while maintaining per-plant spectral, quantity, and intensity characteristics.

[0012] In some embodiments, in addition to the sensors on the lighting and control posts, sensors, illuminators, and the like may be positioned above the plant post so that the sensors have an aerial view of the plant post associated with the plant. In one example, the sensors may include one or more image capture devices positioned above or on top of a downward-facing growing chamber with a view of the area in front of the plant post or plant tower as would be visible to a user opening the door of the housing. In another example, overhead sensors may include multiple sensors positioned near the top of the housing, such as at each corner, corresponding to each sidewall, and the like. In this example, the combination of sensors may provide not only a top-down view of the housing, but also a 360-degree view of the plant post, including a front view.

[0013] In some cases, overhead sensors may be used to track and / or monitor the planting, pruning, harvesting, cleaning, and assembly of seed pods, growth rings, and any other components, life stages, maintenance, or consumables associated with the housing. Sensor data (e.g., image data, etc.) may also be used to assist or guide the farming user experience with the systems described herein. This experience may include an on-board touch-glass interface, a mobile application, audible commands, or any other type of machine-to-human interface. As illustrated, if a user plants a basil plant in the top ring section or row of a planting pole, basil, as a tall growing plant, may impact the top of the growing enclosure. In this example, if the system detects in overhead sensor (or other sensor) data that a basil seed pod is located outside the recommended planting region defined for the planting pole, the system may notify the user via a mobile application. This notification may include planting instructions for relocating the basil seed pod to another lower container within the recommended region. Thus, the system may include many different recommended regions associated with a planting pole. Each recommended region may correspond to a different type or species of plant.

[0014] As one illustrated example, the system may determine the appropriate amount of light for a particular plant from sensor data, for example, by determining the amount of reflection associated with the leaves of a plant in one or more wavelengths (e.g., the infrared spectrum). The system may then adjust the amount, spectrum, and intensity of light so that the leaves absorb within a 100% threshold amount of light provided by rotating the plant pole. Thus, the plant does not receive excess light, and the system reduces overall power consumption compared to traditional indoor growing systems.

[0015] In one particular embodiment, the housing may include one or more sensors and / or illuminators along the top surface or ceiling, in addition to the sensors and / or illuminators associated with the lighting and control pole, to further assist in data capture and provide custom lighting to individual plants to modify taste and nutrition based on user or family preferences.

[0016] In some implementations, the system may be configured to provide data, analytics, and notifications / alerts / messages to an owner or user of the system. For example, the system may wirelessly communicate with a network or user device associated with the owner. The system may analyze captured sensor data for each individual plant to determine its associated life stage and health status. In some cases, the system may provide progress reports, such as growth scorecards, on a periodic basis (e.g., daily, weekly, monthly, etc.), which may be presented to the user via a user device, a mobile device, and / or an associated application hosted by, for example, the user's mobile device. In some cases, the periodic basis may be determined based on the type and species of plants in the enclosure, the age or life stage of the plants in the enclosure, the number of plants in the enclosure, and / or a combination thereof and may be defined by the user.

[0017] In other examples, the notification, alert, or message may include a three-dimensional model of the planting pole and each plant within the housing. In some cases, the three-dimensional model may accurately represent the location, size, shape, and current status of individual plants, such as at a particular point in time. In these cases, a user may be able to view the model not only from a 360-degree view via a user interface, such as a user device, but also over time (e.g., via an elapsed or adjustable time scale). In some particular examples, the system may record the three-dimensional model per predetermined number of rotations of the planting pole (e.g., 1, 3, 5, and 10, etc.) and / or per predetermined period (e.g., every 10 minutes, every hour, every day, every week, etc.). In some cases, the three-dimensional model may include multiple views (e.g., heat maps) that may represent the state of the plant (e.g., health, maturity, exposure time, exposure wavelength, exposure intensity, etc.), allowing a user to quickly view the progress, status, and changes of the plant within the enclosure.

[0018] In some embodiments, the system may also determine whether there are any concerns or problems with the health and well-being of the plant. For example, if the system detects wilting, unusual reflections, reduced absorption, drooping, and the like associated with the plant, the system may generate a notification or alert so that the user can inspect or intervene in the plant's health. For example, if the plant is diseased or infested with harmful insects, the user may remove the entire plant and / or planting pole to mitigate long-term damage to the system's overall crop output.

[0019] Also, in some implementations, the system may provide a harvest alert or harvest message to the user for each individual plant. For example, the system may determine, based on sensor data, that a plant has reached between 90 and 95 percent of its maximum growth and needs to be harvested to improve overall system yields and optimize flavor (e.g., to prevent bitterness, which can occur when plants begin to rot or become stressed). In some cases, harvest thresholds (e.g., size, life stage, growth potential, flavor, etc.) may be selected by the system based at least in part on user input, such as the type of preparation (e.g., salad, cooked, dried, etc.) the user plans for a particular plant or group of plants. For example, earlier harvesting of a plant may result in better flavor if eaten raw, while later harvesting may result in increased yield, but later harvesting may be preferable if the plant is to be cooked.

[0020] In some cases, when harvesting is initiated, such as by a user opening a door to the housing, the system may cause the planting post to rotate, tilt, or otherwise adjust its position to orient planting containers that will continue to be ready to harvest plants toward the door opening to facilitate harvesting by the user. In some cases, the system may allow the user to select plants (via an application on the user device and / or a user interface on the housing), and the system may orient the planting post to present containers containing the selected plants at the opening. In some cases, the system may cause the planting post to open the plants selected by the user that are most ready to harvest (e.g., the most mature plants, the most oversized plants, and the plants that need the most pruning, etc.).

[0021] In some cases, the system, or a cloud-based service associated with and in communication with the system, may be configured to generate health, harvest, and taste thresholds for growing individual species and varieties of plants based on past yield and harvest conditions in the system, based on past yield and harvest conditions of other systems, and various user inputs (such as responses to user surveys or notifications, user harvesting preferences, and user food preparation preferences). For example, the system may input sensor data and / or user preferences and habits into one or more machine learning models, which may output various conditions and thresholds associated with the system, such as notification or alert thresholds, plant health thresholds, lighting control thresholds, harvest thresholds, and plant pole rotation speed thresholds. In some cases, the system may provide discard alerts or warnings if the plants are unhealthy or infested in a way that jeopardizes harvest reminders, or if the plants are experiencing unexpectedly slow growth rates (e.g., growth rates that are below a threshold amount based on the type or species, age, etc. of the particular plant). 369).

[0022] In some particular examples, the system, or cloud-based service, may determine an estimated harvest yield for a user from the sensor data. The estimated yield may include a range and / or a range of yields based on usage and / or harvest time. In some cases, the estimated yield may include data associated with a range of amounts based on the user's taste preferences (e.g., longer growing seasons result in higher yields but increased bitterness in greens).

[0023] In one particular example, the system may use machine learning models to perform object detection and object classification for plants. For example, one or more neural networks may generate any number of learned inferences or heads. In some cases, the neural networks may be end-to-end trained network architectures. In one example, the machine learning models may include segmenting and / or classifying extracted deep convolutional features in the sensor data into semantic data (e.g., rigidity, light absorption, color, health, life stage, etc.). In some cases, the system outputs the appropriate truth for the model in the form of per-pixel semantic classifications (e.g., foliage, stem, fruit, vegetable, bug, and decay, etc.).

[0024] In some cases, the planting pods may be marked with a visible or invisible spectrum (e.g., infrared spectrum) that the system may read upon insertion of the pod into the planting container. This marking may indicate the type or species of plant associated with the planting pod, as well as other information such as the age of the pod. In other cases, the planting container of the planting pole may include an electrical or magnetic coupling that allows the system to detect the insertion and determine information associated with the pod upon insertion.

[0025] In some examples, the cloud-based system may be configured to receive and aggregate data associated with multiple enclosures. In some cases, the cloud-based system may process plant-associated data received from each of the multiple enclosures to determine adjustments to the specific parameters of various sensors and systems in the enclosures. For example, the cloud-based system may apply one or more machine learning models, as described above and below, to determine parameters associated with sensors that may be adjusted in future models or units of the enclosures. For example, the cloud-based system may input captured data into a machine learning model, which may output adaptations for use with the lens, focus, shutters, etc. in the sensors. The cloud-based system may also output configurable or adjustable characteristics (such as lighting parameters, humidity or moisture parameters, and dynamic sensor settings) that may be downloaded or applied to one or more active enclosures.

[0026] 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 any number of layers (convolutional or not). As can be understood in the context of this disclosure, neural networks can utilize machine learning, which can refer to a broad class of such algorithms in which output is generated based on learned parameters.

[0027] Although described in the context of neural networks, any type of machine learning may be used consistently in accordance with this disclosure.For example, machine learning algorithms include 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), etc.), instance-based algorithms (e.g., ridge regression, least absolute shrinkage and selection operator (LASSO), elastic net, least angle regression (LARS), etc.), decision tree algorithms (e.g., classification and regression trees (CART), iterative dichotomization (DDD)), etc. 3) (ID3), Chi-squared Automatic Interaction Detection (CHAID), Decision Stamps, Conditional Decision Trees, etc.), Bayesian algorithms (e.g., Naive Bayes, Gaussian Naive Bayes, Multinomial Naive Bayes, Average One-Dependent Estimator (AODE)), Bayesian Belief Networks (BNN), Bayesian Networks), Clustering algorithms (e.g., k-means, k-medians, Expectation Maximization (EM), Hierarchical Clustering), Association Rule Learning Algorithms (e.g., Perceptron, Backpropagation, Hopfield Networks, Radial Basis Function Networks (RBFN), etc.), Deep Learning Algorithms (e.g., Deep Boltzmann Machines (DBM), Deep Belief Networks (DBN), Convolutional Neural Networks These include, but are not limited to, neural networks (CNNs), 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), mixed discriminant analysis (MDA), quadratic discriminant analysis (QDA), flexible discriminant analysis (FDA), etc.), ensemble algorithms (e.g., boosting, bootstrap aggregation (Bagging), AdaBoost, blending, gradient boosting machines (GBM), gradient boosted regression trees (GBRT), random forests, etc.), support vector machines (SVMs), supervised learning, unsupervised learning, and semi-supervised learning.Additional example architectures include ResNet50, ResNet101, VGG, DenseNet, PointNet, etc. In some cases, the system may also apply Gaussian blurs, Bayesian functions, color analysis or processing techniques, and / or combinations thereof.

[0028] In one particular example, upon initialization or installation of the planting pole and / or lighting and control pole, the system may perform a lactonization process. For example, the sensor system may capture an image set or frame set of sensor data. The system may detect markers within the field of view of the sensor system based at least in part on the image set. In this example, each detected marker may indicate the position (e.g., three-dimensional position and three-dimensional rotation, etc.) of the capturing sensor relative to the frame of the housing or, for example, the base of the planting pole. The system may then perform an error minimization technique (e.g., least squares techniques, etc.) based at least in part on a known model of the housing and sensor position to determine the sensor position relative to the frame and planting pole. The system may then combine the sensor position relative to the frame and the planting pole to determine the sensor's final position relative to the frame and planting pole.

[0029] The system may then determine the position of the sensor relative to the individual planting container based on the final position of the sensor relative to the frame and planting tower and a known model for the planting pole. In some cases, the system may also determine the position of one or more illuminators or emitters relative to the individual planting container by combining the position of the sensor relative to the individual planting container with a known transformation (such as a six degree of freedom transform). As such, the system may prescribe or provide individualized lighting characteristics to each individual plant within each individual planting container.

[0030] In some particular examples, the system may identify individual plants within a planting pole using image data generated by an imaging device on a lighting and control pole. For example, the system may capture one or more images or frames of the planting pole. The system may then determine the position of each individual plant relative to its known position relative to the imaging device. For example, the system may use geometric calculations to project the plant's position or location onto the frame of the imaging device. The system may then select a region of interest (e.g., a rectangle, a trapezoid, a customized shape based on the plant's bounding box, etc.) associated with the individual plant's position based at least in part on the frame of the imaging device. The system may label pixels in the region of interest using semantic segmentation and / or semantic classification techniques. For example, the system may input image data within the region of interest into a machine learning model and receive the plant's type or species, age, health status, etc. as output from the machine learning model. The system may then assign the machine learning model's data output, such as metadata for the image data, to each pixel in the region of interest.

[0031] In other particular examples, the system may be configured to disable or turn off any lights or illuminators within the enclosure. Thus, the system may reduce ambient light associated with the enclosure. In some cases, the system may be configured to perform the following actions at certain times of day (e.g., nighttime) to further reduce ambient light within the enclosure: In other cases, the system may close, tint, frost, reduce transparency, or otherwise shade the interior of the enclosure. The system may engage or activate spectral sensors and desired illuminators or emitters (e.g., infrared illuminators). The system may also rotate the planting pole while the sensors and illuminators are engaged to generate image data associated with the entire surface of the planting pole. The system may perform segmentation and / or classification on the image data for each planting container, as described above. In some cases, based on the output of the segmentation and / or classification network, the system may determine a plant position associated with each planting container that maximizes the number of pixels corresponding to each individual plant. In some cases, the system may utilize a sliding window representation of the illuminator or emitter's field of view on the segmented and / or classified image data. The system may then determine the number of pixels for each plant in each planting container. In an optional step of the example process, the system may deactivate (e.g., turn off) the illuminator or emitter and have the spectral sensor capture baseline reflectance data associated with one or more individual plants.

[0032] In the present illustrative example, the system may then articulate or position the illuminators or emitters so that their fields of view are associated with the pixels determined above. The illuminators or emitters may then be engaged (or reengaged) for a desired period of time (e.g., a period selected based on the type, age, health, etc. of the associated plant) and at a desired spectrum or wavelength (e.g., near-infrared, infrared, ultraviolet, visible, etc.). The spectral sensors may capture additional sensor and / or image data associated with the plants during that period. The system may then determine reflected response data at various spectra and / or wavelengths. The system may then subtract baseline reflectance data from the reflected response data for each individual plant. The system may then utilize the resulting reflectance data to determine the health, age, or other condition of the plants.

[0033] In some implementations, the system may also assist a user in selecting a location or container for a plant or seed pod using a model, such as a three-dimensional model of expected plant growth based on the planting post and the expected rotation of the planting post. For example, the system may suggest a container for a particular type of plant based on past or historical performance or growth data, rotation data associated with the planting post, known lighting conditions associated with the enclosure, etc. In one example, the system may capture sensor and / or image data on the planting post and the plant associated with the planting post to determine plant growth rate and estimated yield and detect health issues (such as wilting). The system may also generate a model of the planting post and planting container, such as a three-dimensional model. This model may be used to determine optimal locations for particular types of plants to produce better results. In some cases, the model may be specific to each enclosure, while in other cases, the model may be generic across multiple enclosures and generated based on aggregated sensor data.

[0034] In some cases, the model may be integrated with or accessible via an associated application hosted on a personal electronic device that communicates wirelessly with the housing and / or cloud-based service. The application may enable the personal electronic device to display the 3D model of the currently inserted plant over time, such as from a current state to a future state. In some cases, the model may be rotatable, such as around a planting pole, such as via a swipe or other touch-based gesture.

[0035] 1-5 illustrate example views of an enclosure 100 for providing a controlled growing environment, according to some implementations. The enclosure 100 may be configured as a plant growing apparatus that provides a climate-controlled interior that houses at least one plant housing assembly or planting pole 108. However, unlike traditional home gardening systems that provide uniform lighting and temperature, the enclosure 100 may provide active monitoring and adaptive environmental conditions based on health, growth stage, plant type or species, etc., via one or more systems either internal to the enclosure 100, collocated within a physical environment such as a home 114, or remote, cloud-based system 116.

[0036] In some particular embodiments, the enclosure 100 may be configured to monitor individual plants within a growing environment and provide tailored growing conditions, such as custom lighting (e.g., length of exposure, focal length, temperature, specific wavelength, intensity, and amount, etc.) For example, the enclosure 100 may include one or more illuminators 102 (or light sources) associated with or positioned relative to one or more lighting and control posts 104.

[0037] In some implementations, a lighting and control column (or panel) 104 is located within the enclosure 100. , or along specific regions of the enclosure 100. In addition to one or more illuminators 102, the lighting and control pole 104 may be equipped with various sensors 106 for monitoring individual plants. For example, the lighting and control pole 104 may include one or more sensors 106, such as an imaging device (e.g., an RGB (red-green-blue) imaging device, an infrared imaging device, a monochrome imaging device, a lidar device, etc.), a humidity sensor, a temperature sensor, a barometric pressure sensor, air quality / particular sensors, a gas sensor, a carbon dioxide (CO2) sensor, and a spectral sensor, to generate sensor data 118 associated with the interior of the enclosure 100.

[0038] As described above, the lighting and control pole 104 may be equipped with one or more illuminators 102 (visible light, infrared illuminators, ultraviolet light, etc.). The illuminators 102 may be adjustable to provide specific spectra, amounts of light, and light intensities to individual planting containers based on the health, life stage, size, and type or species of the corresponding plant.

[0039] In some cases, the lighting and control post 104 may include multiple rows or columns of sensors 106 and / or illuminators 102. For example, the lighting control post 104 may include a top row (or column) of sensors 106 and / or illuminators 102, a middle row (or column) of sensors 106 and / or illuminators 102, and a bottom row (or column) of sensors 106 and / or illuminators 102. In other cases, the lighting and control post 104 may include a row or column of sensors 106 and / or illuminators 104 for each corresponding row or column of plants.

[0040] In some implementations, the field of view or region of interest associated with each sensor 106 and / or illuminator 102 may be adjustable so that a single sensor 106 and / or illuminator 102 may capture data and provide light to multiple planting locations or containers while maintaining the spectral, quantity, and intensity characteristics of each plant. For example, individual growing conditions (e.g., health, size, life stage, species, etc.) may be detected or determined for each plant.

[0041] For example, the housing 100 may include a planting post or tower 108 within the housing 100. The planting post 108 may include a plurality of containers, generally designated 110, configured to house individual plants. The planting containers 110 may be arranged in both vertical columns and horizontal rows around the planting post 108. For example, in one particular example, the planting post 110 may include 20 columns and 5 rows of planting containers. In some cases, the planting containers 110 may be staggered between rows, with each column having one planting container in every other row. In these cases, staggering the planting containers 110 allows the housing 100 to monitor the individual plants while ensuring that the individual plants have sufficient room to grow.

[0042] In some cases, the planting post 108 may be rotatable 360 ​​degrees around the base within the housing 100, or any other limited rotation. For example, the drive motor may be configured to mechanically or magnetically rotate the planting post 110 within the housing 100 based on one or more control signals or configuration data 120, such as from the system 116 in some examples (or via an internal control system of the housing 100 in other examples). In some cases, each individual planting container 110 may be assigned a unique identifier so that as the planting post 108 rotates, the housing 100 can track each plant based on its determined position within the planting post 108. The planting post 108 can then rotate the planting container 110 toward the door 112 for user access.

[0043] In these examples, the lighting and control post 104 may capture sensor data 118 usable to determine the assigned location of a plant as it is inserted or planted within a particular planting container 110. For example, the planting container 110 may have visible or invisible markings (e.g., infrared spectrum markings, etc.) that may enable the lighting and control post 104 to capture data 118 usable to determine the insertion of a planting pod into the planting container 110 and the corresponding receptacle identifier and / or location on the planting post 108. In other cases, the captured sensor data 118 may be usable to determine when the planting post 108 is filled as the planting post 108 rotates. In some cases, location markings may be placed at various locations around the interior surface of the enclosure 100 and / or the top and bottom of the planting post 108 to assist in initialization or location determination upon restart or reboot of the enclosure 100, as well as in response to an updated or replaced lighting and control post being installed or calibrated.

[0044] In some embodiments, in addition to the sensors 106 and / or illuminators 102 of the lighting and control pole 108, sensors, illuminators, etc. may be positioned above the plant pole 108 so that the sensors have an aerial view of the plant pole 108. In one example, the sensors 106 may include one or more image capture devices positioned above or on top of the growing room facing downward, relative to a view of the area forward of the plant pole or plant tower 108 as would be visible to a user opening the door 112 of the housing 100. In another example, the overhead sensors 106 may include multiple sensor types or instances positioned near the top of the housing, such as at each corner, corresponding to each sidewall, etc. In this example, the combination of sensors 106 may provide a 360-degree view of the plant pole 108, including not only a top view of the housing 100, but also a front view.

[0045] In some cases, overhead sensors 106 may be used to track and / or monitor planting, pruning, harvesting, cleaning, and assembly of seed pods, growth rings, and other components, life stages, maintenance, or consumables associated with enclosure 100. Sensor data 118 (e.g., image data, etc.) may also be used to assist or guide the agricultural user experience with enclosure 100. This experience may include an on-board touch-glass interface (such as one built into door 112 of enclosure 100), a mobile application (accessible via a remote mobile device), audible commands, or any other type of machine-to-human interaction.

[0046] As an illustrated example, suppose a user plants basil seedlings in the upper circular section or row of the planting pole 108. As a tall growing plant, basil may impact the upper portion of the growing enclosure 100. In this example, if a system 116 (e.g., an on-board or cloud-based system) associated with the enclosure 100 detects in the sensor data 118 that the basil seed pods have been placed outside of the recommended planting area referenced to the planting pole 108, the system 116 may notify the user via a mobile application. This notification may include planting instructions for relocating the basil seed pods to another lower container 110 within the recommended area. As such, the system may include many different recommended areas associated with the planting pole 108. Each of the recommended areas may correspond to a different type or species of plant.

[0047] In other cases, the system 116 associated with the housing 100 may determine the amount of light that would be appropriate for a particular plant, for example, by determining from the sensor data 118 the amount of reflectance associated with the plant's leaves within one or more wavelengths (such as the infrared spectrum). The system may then determine the amount of light that would be appropriate for the plant, such that the leaves are absorbing within a 100% threshold amount of light provided. , the amount, spectrum, and intensity of light can be adjusted so that plants do not receive too much light, and the system 116 reduces overall power consumption compared to traditional indoor growing enclosures.

[0048] In some implementations, the system 116 may also be configured to provide data, analysis, and notifications / alerts to an owner or user of the system 116. For example, the system 116 may communicate wirelessly with the network 120 or a user device 122 associated with the user 124. The system 116 may analyze the sensor data 118 captured regarding individual plants to determine the life stage and health status associated with the individual plants. In some cases, the system 116 may provide periodic (e.g., daily, weekly, monthly, etc.) progress reports, such as a growth scorecard, that may be presented to the user 124 via the user device 122 and / or an associated application hosted by the user device 122, for example. In some cases, the periodic criteria may be determined based on the type and species of plants within the housing 100, the age or life stage of the plants within the housing 100, the number of plants within the housing 100, and / or a combination thereof, and may be defined by the user 124.

[0049] In some cases, system 116 may determine whether there are any concerns or issues regarding the health and well-being of the plant. For example, system 116 may detect wilting, abnormal reflectance, reduced absorption, drooping, and the like associated with the plant, and system 116 may generate notifications 126 or alerts 128 to user device 122 so that user 124 can inspect or intervene in the plant's health. For example, if a plant becomes diseased or introduces harmful insects, user 124 may remove the entire plant and / or planting pole to mitigate long-term damage to the enclosure 100's overall crop production.

[0050] In some embodiments, system 116 may also provide harvest alerts to user 124 for each individual plant. For example, system 116 may determine, based on sensor data 118, that a plant has reached between 90 and 95 percent of its maximum growth and should be harvested to improve the overall yield of enclosure 100 and optimize flavor (e.g., to prevent bitterness, which can occur when plants begin to rot or become stressed). In some cases, harvest thresholds (e.g., size, life stage, growth potential, flavor, etc.) may be selected by system 116 based at least in part on user input, such as the type of preparation (e.g., salad, cooked, dried, etc.) the user plans for a particular plant or group of plants. For example, early harvesting of a plant may improve flavor if the plant is eaten raw, while late harvesting may increase yield, although a later harvest may be preferable if the plant is to be cooked.

[0051] In some cases, the housing 100, or a cloud-based service 116 associated with and in communication with the housing 100, may be configured to generate health, harvest, and taste thresholds for the growth of individual plant species and varieties based on various user inputs (such as responses to user surveys or notifications, user harvesting preferences, and user food preparation preferences), historical yield and harvest conditions of the housing 100, historical yield and harvest conditions of other housings 100, etc. For example, the system 116 may input the sensor data 118 and / or user preferences and habits into one or more machine learning models, which may output various conditions and thresholds associated with the system, such as notification or alert thresholds, plant health thresholds, lighting control thresholds, and harvest thresholds. In some cases, the system 116 may provide discard alerts 128 or discard warnings, such as when a plant is unhealthy or infested in a way that jeopardizes a harvest warning, or when there is an unexpectedly slow growth rate (e.g., a growth rate that is below a threshold amount based on the type or species, age, etc. of the particular plant).

[0052] In some particular examples, the housing 100, or the cloud-based service 116, may determine an estimated yield of the harvested crop for the user 124 from the sensor data 118. The estimated yield may include a range and / or different yields based on usage and / or harvest time. In some cases, the estimated yield may include data associated with different amounts based on the user's taste preferences (e.g., a longer growing period results in a higher yield but an increased bitterness in the greens).

[0053] In one particular example, the system 116 may also perform object detection and object classification for plants using a machine learning model. For example, one or more neural networks may generate any number of learned inferences or brains. In some cases, the neural network may be an end-to-end trained network architecture. In one example, the machine learning model may include segmenting and / or classifying extracted deep convolutional features of the sensor data into semantic data (e.g., stiffness, light absorption, color, health, life stage, etc.). In some cases, the model outputs the appropriate truth in the form of a pixel-by-pixel semantic classification (e.g., leaf, stem, fruit, vegetable, insect, rot, etc.).

[0054] In some cases, the planting pods may be marked with a visible or invisible spectrum (e.g., infrared spectrum, etc.) that the sensor 106 may read when the pod is inserted into the planting container. This marking may indicate the type or species of plant associated with the planting pod, as well as other information such as the age of the pod. In other cases, the planting container of the planting pole may include an electrical or magnetic coupling that enables the system 116 to detect the insertion and determine information associated with the pod upon insertion.

[0055] In some examples, the cloud-based system 116 is configured to receive and aggregate data associated with multiple housings 100. In some cases, the cloud-based system 116 may process the plant-associated data received from each of the multiple housings 100 to determine adjustments to the intrinsic parameters or setting data 130 of the various sensors 106 and internal components of the housing 100. For example, the cloud-based system may apply one or more machine learning models, as described above and below, to determine parameters and / or setting data 130 associated with the internal components of the housing 100 (e.g., water delivery system, nutrient delivery system, lighting system, rotation system, etc.) that may be adjusted in future models or units of the housing 100. For example, the cloud-based system 116 may input captured sensor data 118 into a machine learning model, which may output adaptations for use in the sensor's lens, focus, shutter, etc. The cloud-based system 116 may also output configurable or adjustable characteristics (such as lighting parameters, humidity or moisture parameters, and dynamic sensor settings) that may be downloaded or applied to one or more active enclosures 100 based on specific user input, the performance history of a particular enclosure 100, and external sensor data (e.g., temperature or lighting conditions in the home 114).

[0056] In one particular example, upon initialization or installation of the planting pole 108 and / or the lighting and control pole 104, the housing 100, or a system associated with the housing 100, may perform an initialization process. For example, the sensor system may capture an image set or frame set of sensor data. The system may detect markers within the field of view of the sensor system based at least in part on the image set. In this example, each detected marker may indicate the position (e.g., three-dimensional position and rotation, etc.) of the capturing sensor relative to the frame of the housing, or, for example, the base 122 of the planting pole 108. The system may then perform an error minimization technique (e.g., least-squares technique, etc.) based at least in part on a known model of the housing and sensor position to determine the sensor position relative to the frame and the planting pole 108. The system may then combine the sensor position relative to the frame and the planting pole 108 to determine the sensor's final position relative to the frame and the planting pole 108.

[0057] The system 116 may then determine the position of the sensors 106 relative to the individual planting containers 110 based on the final position of each individual sensor 106 relative to the frame and planting posts 108 and known models for the planting posts 108. In some cases, the enclosure 100 or the system 116 may select a model for the planting post 108 based on the captured sensor data and a known set of characteristics for planting posts 108 that may be present in the enclosure 100. For example, if multiple planting post 108 designs are available, the system 116 may determine the type and / or class of planting posts 108 present in the enclosure 100, as well as the number of planting posts 108.

[0058] In some cases, the system 116 may determine the position of one or more illuminators 102 or emitters relative to each of the individual planting containers 110 by combining the positions of the sensors 106 and / or illuminators 102 relative to the individual planting containers 110 with a known transformation between the positions of the sensors 106 and the positions of the illuminators 102 (such as a six-degree-of-freedom transformation). As such, the system 116 may then prescribe or provide individualized lighting characteristics to each of the individual plants within each of the individual planting containers 110.

[0059] In some particular examples, the system 116 may use sensor data (e.g., image data) generated by the sensors 106 of the lighting and control mast 104 to identify individual plants within the planting pole 108. For example, the system 116 may capture one or more images or frames of the planting pole 108. The system 116 may then determine the position of each individual plant relative to known positions for the individual sensors 106. For example, the system may use geometric calculations to project the position or location of the plants onto the frame of the sensor 106. The system 116 may then select a region of interest or determine a bounding box associated with the location of the individual plants based at least in part on the frame. The system 116 may label pixels in the region of interest using semantic segmentation and / or classification techniques. For example, the system 116 may input the sensor data 118 within the region of interest into a machine learning model and receive the plant type or species, age, health, etc. as output from the machine learning model. The system 116 may then assign the data output of the machine learning model to each pixel in the region of interest, such as as metadata for the sensor data 118.

[0060] In other particular examples, the system 116 may be configured to disable or turn off any lights or illuminators 102 within the enclosure 100. As such, the system 116 may reduce ambient light associated with the enclosure 100. In some cases, the system 116 may be configured to perform the following actions during certain times of day (e.g., nighttime) to further reduce ambient light within the enclosure 100: In other cases, the system 116 may cause window coverings on the door 112 to close, tint, or otherwise shade the interior of the enclosure 100. The system 116 may interface with or activate spectral sensors and desired illuminators or emitters (e.g., infrared illuminators).

[0061] The system 116 may also rotate the planting pole 108 while the sensors 106 and illuminators 102 are coordinated to generate sensor data 118 associated with the entire surface of the planting pole 108 (e.g., via the provided configuration data 130). The system 116 may perform segmentation and / or classification on the sensor data for each planting container 110. In some cases, based on the output of the segmentation and / or classification network, the system 116 may determine a plant position associated with each planting container 110 that maximizes the number of pixels corresponding to each individual plant. In some cases, the system 116 may utilize a sliding window representation of the illuminator or emitter field of view on the segmented and / or classified image data. The system 116 may then determine the number of pixels for each plant in each planting container 110. In any step of the example process, the system 116 may disengage (e.g., turn off) the illuminator or emitter 102 and cause the spectral sensor to capture baseline reflectance data associated with one or more individual plants (e.g., via configuration data).

[0062] In the present particular example, the system 116 may articulate or position the illuminators or emitters 102 so that the field of view of the illuminators or emitters 102 is associated with the pixels determined above. The illuminators or emitters 102 may then be engaged (or re-engaged) for a desired period of time (e.g., a period selected based on the type, age, health, etc. of the associated plant) and at a desired spectrum or wavelength (e.g., near-infrared, infrared, ultraviolet, visible, etc.). The sensor 106, such as a spectral sensor, may capture additional sensor and / or image data associated with the plant during that period of time. The system 116 may then determine reflectance response data at various spectra and / or wavelengths. The system 116 may then subtract the baseline reflectance data from the reflectance response data for each individual plant. The system 116 may then utilize the resulting reflectance data to determine the health, age, or other condition of the plant.

[0063] In some embodiments, the system 116 may also assist the user in selecting a location or container for planting a plant or seed pod using a model, such as a three-dimensional model of the planting post and expected plant growth based on the expected rotation of the planting post 108. For example, the system may suggest a container 110 for a particular type of plant based on past or historical performance or growth data, rotation data associated with the planting post 108, known lighting conditions associated with the enclosure 100, etc. In one example, the system 116 may capture sensor data and / or image data of the planting post 108 and its associated plant to determine the plant's growth rate and estimated yield and detect health issues (such as wilting). The system 116 may also generate a model, such as a three-dimensional model of the planting post 108 and the container 110. This model may be used to determine the optimal location or location for a particular type of plant to produce better results. In some cases, the model may be specific to each enclosure 100, while in other cases, the model may be generic across multiple enclosures 100 and generated based on aggregated sensor data 118.

[0064] In some cases, the model may be integrated or accessible via an associated application hosted on the housing 100 and / or the user device 122 that communicates wirelessly with the cloud-based service 116. The application may enable the user device 122 to display a 3D model, such as a currently inserted plant, over time, such as from a current state to a future state. In some cases, the model may be rotatable around the planting pole 108, such as via a swipe or other touch-based action.

[0065] FIG. 6 illustrates a front view 600 showing an example of a planting post 108 associated with the housing 100, according to some embodiments. In the illustrated example, the planting post 108 may include a plurality of containers 110, generally designated 602, configured to house individual plants. The planting containers 110 may be arranged in both vertical columns and horizontal rows around the planting post 108. For example, in one particular example, the planting post 108 may include 20 columns and 5 rows of planting containers 110. In some cases, the planting containers 110 may be staggered between rows, with each column having one planting container 110 in every other row. In these cases, staggering the planting containers 110 allows the housing to monitor each of the individual plants 602 while ensuring that each individual plant 602 has sufficient room to grow.

[0066] In some cases, the planting post 108 may be rotatable 360 ​​degrees around a base within the housing, or may be capable of any other limited rotation. In some cases, each individual planting container 110 may be assigned a unique identifier so that a system, such as the system 116 in FIGS. 1-5 , can track each plant 602 based on its determined position within the planting post 108 as the planting post 108 rotates. In these cases, the system may determine the plant's assigned position when inserting or planting the plant into the particular planting container. For example, the planting container 110 may have visible or invisible markings (e.g., infrared spectrum marks, etc.), typically indicated by 604, that the system 116 may read upon insertion of a planting pod or seed cartridge. In other cases, the system 116 may determine that the container 110 is full as the planting post 108 rotates. In some cases, location markings 604 may be placed on the interior surface of the housing and / or in various locations near the top and bottom of the planting pole 108 to assist in initialization or location determination upon restarting or rebooting the system, and in response to updating or replacing lighting and control poles that have been installed or calibrated.

[0067] FIG. 7 illustrates an exploded view 700 showing an example planting post 108 of the housing 100, according to some embodiments. In the current example, the planting post may include multiple rings 702 stacked on top of each other. Each ring 702 may have multiple planting containers 110, which may be arranged in rows along the ring 702. The rings 702 may be configured to fit together via locking mechanisms 704 and 706 to ensure sufficient space between the containers 110 for plant growth. As such, the number of rings 702 may be adjusted to accommodate the size of the housing. Additionally, the tallest portion of each ring 702 may vary to accommodate the planting of different sized plants and / or the insertion of different sized seed pods or cartridges.

[0068] In some cases, a gasket 708 may be placed between each subsequent or stacked ring 702 to reduce vibration and movement as the plant post 108 rotates. The bottom or ring may include a downwardly extending drain member 710 to provide a location for fluid to drain from the interior of the plant post 108 to, for example, a reservoir located below the plant post 108.

[0069] 8 is a pictorial diagram 800 illustrating an example front view of a planting pole associated with the housing in FIGS. 1 and 2 and a lighting and control pole 104, according to some implementations. In the illustrated example, the planting pole 108 includes a plurality of planting receptacles, generally shown as planting receptacles 110(A) through (H). Each planting receptacle 110 may be configured to receive a seed cartridge or pod, such as the seed cartridge described below with respect to FIG. 12, but the planting receptacles 110 may be arranged such that each receptacle 110 provides space or room above the receptacle 110 for plants to mature.

[0070] In the current example, the lighting and control pole 104 is vertically positioned and may include one or more sensors, such as sensors 106(A) and 106(B), and one or more illuminators, such as illuminator 102. In this example, sensor 106(A) may be a spectral sensor, and sensor 106(B) may be an image sensor. Each of the sensors 106 may have a corresponding field of view 802(A) and 802(B) of the planting pole 108, as shown. Similarly, illuminator 102 may have a field of illumination 804. In this example, illumination field 804 may be configured to provide directed illumination to a single plant associated with a particular planting container (currently illustrated as planting container 110(B)). In this example, the characteristics of the light emitted by illuminator 102 and the location of illumination field 804 may be adjustable based on a current target (e.g., planting container 110(B)). For example, because detailed container 110(B) may have different vegetation at different maturity levels or life stages and accordingly require different lighting for optimal growth, the intensity, wavelength, and type of lighting may vary as the illumination area 804 is adjusted from planting container 110(B) to planting container 110(A) as shown.

[0071] In the current example, the planting pole 108 may include one or more markers 806 that may be visible to the sensor 106 as the planting pole 108 rotates. The markers 806 may assist the system in determining the currently visible planting container 110 and the current position of the planting pole 108 as it rotates about its vertical axis. The sensors 106 and / or illuminators 102 may have known positions along the lighting and control pole 108 such that the system may be able to determine the space and / or location within the field of view of the sensor associated with each planting container 110, and thereby each plant. The sensors 106 and illuminators 102 may also have known distances that may be usable by the system to determine adjustments to the illumination area 804 to precisely target specific plants with specific lighting based on the determined plant positions, the planting containers 110 determined within the field of view of the sensors 106, and the distances between the respective sensors 106 and / or the distances between the illuminators 103 and the sensors 106.

[0072] In some cases, the system may also utilize the geometry of the housing and / or planting post 108 to determine the location of the illumination area 804. The system may also utilize the known distance between the sensor 106 and the planting post 108, as well as the known distance between the illuminator 102 and the planting post 108, to assist in adjusting the illumination area 804. In some cases, the illuminator may include pan, tilt, and zoom capabilities that also enable the illuminator 102 to adjust the position and size of the illumination area 804 based on a target area or location associated with an individual plant.

[0073] 9 is a pictorial diagram illustrating an example top view of a planting pole 108 and a lighting and control pole 104 associated with the housing in FIGS. 1 and 2, according to some implementations. In this example, the lighting and control pole 104 may be configured horizontally within the housing 100, and / or the sensors 106 and illuminators 102 may be offset from one another along a horizontal access instead of or in addition to being vertically offset, as shown above with respect to FIG. 8. For example, the sensors 106 and illuminators 102 may be offset from one another both vertically and horizontally.

[0074] In this example, the system may be able to determine the space and / or location within the field of view 802 of each planting container 110 and the sensor 106 associated with each planting container and therefore each plant. The sensors 106 and illuminators 102 may also have a known horizontal distance that may be usable by the system to determine adjustments to the illumination area 804 to precisely target specific plants with specific lighting based on the determined plant locations, the planting containers 110 determined within the field of view of the sensors 106, and the distances between the respective sensors 106 and / or the distances between the illuminators 103 and the sensors 106. In some cases, the system may utilize the geometry of the housing and / or planting pole 108 to determine the location of the illumination area 804.

[0075] In some cases, the system may utilize sensor data generated by the sensors 106 to determine the type of plant, the health of the plant, the life stage or maturity of the plant, and the size of the plant within each planting container 110. The determined type, health, life stage, size, etc. may then be used by the system to select the characteristics of the light (e.g., intensity, wavelength, illumination area 802, etc.) provided to each plant by the illuminator 102.

[0076] FIG. 10 is a perspective view illustrating an example seed cartridge 1000 for use with a planting post associated with the housing in FIG. 1 , according to some embodiments. The seed cartridge 1000 may be configured to fit or mate with the planting receptacle of the planting post. In some cases, the planting cartridge 1000 may contain seeds, grow medium, nutrients, growth stimulants, hormones, fungi, etc., associated with growing plants in the housing environment. In some cases, the planting cartridge 1000 may include one or more markings 1002 that may be generated by a sensor in the housing and represented in sensor data utilized by the housing and / or system to determine the type of plant inserted into the planting post. That type may then be used to customize lighting (e.g., wavelength, time of day, intensity, etc.) directed to the associated planting post, as described above.

[0077] 11 is a perspective view 1100 illustrating an example of a seed cartridge 1102 engaged with a planting container 110(A) of a planting post 108 associated with the housing in FIG. 1 , according to some embodiments. In this example, as shown, a plant 1104 is sprouting in the space above the planting container 110(A). As such, the system may direct customized lighting to a location above the plant 1104 and / or the planting container 110(A), as described above.

[0078] In this example, the planting container 110(A) includes a marker or identifier 1106 that may be detected in sensor data collected by a sensor system associated with the housing. In some cases, the identifier 1106 may be infrared or invisible to humans to improve the ascetic quality of the planting pole 108 and / or the housing. The identifier 1106 may be used to determine the location of the plant 1104 relative to the planting pole 108. This current example also includes an artificial plant 1108 inserted into the planting container 110(B). In some implementations, the system may monitor for an insertion event associated with the artificial plant 1108 and utilize the detection of the artificial plant 1108 in the sensor data generated by the housing to determine the location or position of the plant 1104 relative to the planting pole 108. In this manner, the artificial plant 1108 may be used not only as a visual indication to a user of the housing, but also for a system associated with the housing in determining the location of a particular plant relative to the planting pole 108. For example, a user may insert one or more artificial plants 1108 into the container 110, each of which may be of a different pattern, color, size, flower type, etc., to provide a visual indication to the user and to a system associated with the housing. As an illustrated example, the visual indication may include detecting movement associated with a gasket flap covering the planting container 110 or a cover for the gasket flap.

[0079] In some examples, the system may also detect an insertion event via detection, scanning, and / or imaging of an identifier (e.g., a barcode, a near field communication (NFC) tag, or a radio frequency identification (RFID) tag, etc.). For example, a user may scan the seed cartridge 1102 before inserting it into the seed container 110(A), as shown. In some cases, scanning via a user device may initiate a sensor scan of the housing to determine the location of the inserted seed cartridge 1102 (e.g., receptacle 110(A), etc.) and / or other events (such as a user-preferred collection event). In some cases, scanning the seed cartridge 1102 with a user device may enable the system to determine the expected plant type, expected growth characteristics, etc.

[0080] 12 is a perspective view 1200 illustrating an example of a seed cartridge 1102 being inserted by a user 124 into a planting receptacle 110(A) of a planting post 108, according to some embodiments. In this example, a sensor system of the housing may be configured to capture sensor data associated with the insertion event, and the housing, and / or a system associated with the housing, may utilize the sensor data representative of the insertion event to determine features and / or characteristics of the seed cartridge 1102 (e.g., plant type, etc.) and a position the seed cartridge has relative to the planting post 108 (e.g., seed cartridge 1102 is within receptacle 110(A), etc.).

[0081] 13-16 are flow diagrams illustrating example processes associated with the growing enclosure, as described above. The processes are illustrated as a collection of blocks in a logical flow diagram, which represent a series of operations, some or all of which may be implemented in hardware, software, or a combination thereof. In the software context, 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, deciphering, compressing, recording, data structures, and the like that perform particular functions or implement particular abstract data types.

[0082] 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 frameworks, architectures, and environments described in examples herein, but the processes may be implemented in a wide variety of other frameworks, architectures, or environments.

[0083] 13 is a flow diagram showing an example of an illustrated process 1300 for determining lighting and control system settings associated with individual plants, according to some implementations. As described above in some cases, the enclosure and associated systems (e.g., control system, cloud-based system, etc.) may be configured to provide customized lighting for each individual plant currently residing in the enclosure. The customized lighting may include custom intensity, wavelength, magnitude, length of time, etc. The customized settings may, in some examples, be selected based at least in part on the determined size, health, life stage, species, maturity, etc. of the plant.

[0084] In step 1302, a first sensor may capture sensor data associated with the housing. In some cases, the first sensor may be an imaging device, such as an RGB (red-green-blue) imaging device, an infrared imaging device, a monochrome imaging device, and a stereo image device, as well as depth sensors and lidar sensors. In some cases, the sensor data may include depth data as well as image data in various spectra.

[0085] In step 1304, the system may detect one or more markers associated with the planting pole (or housing) based at least in part on the sensor data. For example, the markers may be visible or invisible in the human visible spectrum (e.g., in the infrared spectrum) and placed at locations on the planting pole and / or on the surface of the housing. In some cases, the markers may be inserted by a user, such as flower markers, as described above in FIG. 11. In the case of an insertable marker, the system may detect the insertion event and record or save in memory the location or associated planting container along with a model usable for detecting the insertable marker. In some cases, if a marker is removed, the system may detect a removal event and delete the location and model from memory.

[0086] In step 1306, the system may determine a first sensor position relative to the rim of the housing based at least in part on the markers and a known model for the housing. For example, the housing model may be stored with the housing or accessible via a cloud-based service. In some cases, upon initial activation, the system may scan the housing and select a model to use, or the user may select a model.

[0087] In step 1308, the system may determine a first sensor position relative to the planting pole based at least in part on the marker and a known model of the housing. For example, the model of the housing may include characteristics of the planting pole as well as the housing itself. In some cases, the characteristics may change, such as if the planting pole is modular and provides various arrangements of planting containers or various distances between rows and columns of planting containers. In such cases, the system may periodically query the user via the mobile application or housing interface to confirm the placement of the inserted planting pole and / or detect change events in response to updates to the stored housing model associated with the planting pole. In some cases, the user may initiate an update to the housing model via user input on the housing and / or mobile application.

[0088] In step 1310, the system may determine a third position of the first sensor relative to the individual planting containers of the planting pole based at least in part on the first sensor position and the second sensor position. For example, the system may determine the planting containers visible to the first sensor based at least in part on the image data, and then determine the position of the first sensor relative to the individual visible planting containers using the first position and the second position.

[0089] In step 1312, the system may determine a fourth position of the illuminator relative to the planting container based at least in part on the third position and a transform function. The transform function may represent an offset in the X, Y, and / or Z directions between the first sensor and the illuminator. In this example, only the position of the first sensor is used to determine the fourth position between the lighting device and the desired planting container. However, in other cases, the system may utilize additional sensors (e.g., an imaging device and / or a spectral sensor) to determine the position of a second sensor relative to the planting container, and then use the second position and the second transform function to confirm the fourth position and ensure that the appropriate plants are receiving illumination at the desired settings.

[0090] In step 1314, the system may determine at least one setting associated with the illuminator based at least in part on the fourth location and the plant associated with the planting container, and in step 1316, the system may activate the illuminator to provide lighting to the desired plant and / or planting container. For example, the illumination area may be adjusted based on the fourth location so that the illumination area is relative to or aimed at the desired planting container. For example, the illuminator may shift, tilt, and / or expand the field of view of the illumination area to provide customized lighting for the particular plant in the desired planting container. The system may also select light settings or characteristics based on image data captured of the plant. For example, the image data may be used to determine plant characteristics such as health, presence or absence of decay, size, and maturity. The system may then select settings such as intensity, wavelength, and length of time or exposure length based at least in part on the characteristics.

[0091] In some cases, the customized lighting may be configured to provide customized nutrition and / or flavor for each individual plant. For example, a user may input user preferences for the enclosure via an application hosted on the user device and / or via a user input device. The user preferences may include desired flavors (such as sweet and bitter), size, and nutritional benefits (e.g., desired vitamins and fiber). The system may then translate the user preferences into customized lighting (and / or other environmental factors associated with the enclosure and / or individual plants) settings. As an illustrated example, the user preferences, including historical data, plant type, specifics, nutritional values, maturity, and life stage, may be used to determine customized lighting settings for each plant as the plants mature and encourage the plants to reach the user's preferences.

[0092] In some cases, the system may present options for the user to select (either via an application hosted on the user device or via a user interface on the housing) in response to detecting an insertion event. For example, the system may detect the insertion of a particular type of seed cartridge. The system may then query the user regarding desired flavor, size, preparation styles, dish, nutritional goals, etc. The system may then utilize the user input to further customize lighting settings (and / or other environmental factors) associated with the plants.

[0093] In the present example, the process 1300 is described with respect to a planting container, but it should be understood that the process 1300 may be configured to determine the relative positions of plants in addition to, or instead of, the relative positions of the planting containers.

[0094] 14 is a flow diagram illustrating another example of an illustrated process 1400 for determining characteristics of individual plants, according to some implementations. As described above, the system may select illumination characteristics or settings based at least in part on sensor data associated with each individual plant.

[0095] In step 1402, the system may have a sensor capture sensor data associated with the housing. In some cases, the first sensor may be an imaging device, such as an RGB (red-green-blue) imaging device, an infrared imaging device, a monochrome imaging device, a stereo imaging device, and a depth sensor and a lidar sensor. In some cases, the sensor data may include depth data as well as image data in various spectra.

[0096] In step 1404, the system may determine the location of the desired planting container (and / or plant) based at least in part on the sensor data. For example, as described above with respect to process 1300, the system may determine the relative location between the illuminator and the desired planting container. In other cases, the system may utilize a model of the enclosure and / or planting pole, known distances between the illuminator, sensor, and planting pole, and captured sensor data to determine the location of the desired planting container.

[0097] In some cases, a desired planting container may be selected based on a known pattern or, at least in part, on a detected plant associated with the planting container. For example, the system may divide the sensor data into segments associated with each individual planting container and use the sensor data to determine whether a plant or seed cartridge is present. The system may then determine a pattern of illumination to provide illumination to each planting container or plant present in the housing and visible to the sensor and / or illuminator.

[0098] In step 1406, the system may determine a region of interest associated with the planting container (and / or plant). For example, the system may determine the region of interest by detecting the plant and determining one or more boundaries or bounding boxes associated with the plant. In some cases, the bounding boxes may be dynamic based on the size of the plant and / or may be predetermined and associated with individual planting containers.

[0099] In step 1408, the system may provide at least a portion of the sensor data associated with the region of interest to a machine learning model, and in step 1410, the system may receive classification data and / or segmentation data associated with the region of interest from the machine learning model. Then, in step 1412, the system may determine at least one characteristic of the plants associated with the region of interest and the planting container based at least in part on the classification data and segmentation data. For example, the classification data and segmentation data may include boundaries associated with one or more plants in the region of interest, as well as plant species and / or other characteristics of the plants, such as health, size, and maturity. In some cases, the characteristics may include decay, the presence of insects, mold, or other damage. In some cases, the segmentation data may include overlapping plant leaves or other indications that one or more neighboring plants are encroaching on the current region of interest.

[0100] The system may then output at least one feature in step 1414. For example, the feature may be output to a system or module configured to determine lighting or lighting settings based on one or more features in the plants and / or boundaries.

[0101] 15 is a flow diagram illustrating another example of an illustrated process 1500 for determining characteristics of an individual plant, according to some implementations. In some cases, one or more plant characteristics or properties (such as health status) may be determined based on the reflectivity of the plant's leaves within the enclosure.

[0102] In step 1502, the system may disable illuminators and block the viewing windows of the housing. For example, the system may disable any lighting within the housing. Similarly, the system may cause the viewing windows to become tinted or frosted, etc., and / or have a screen lowered. In this way, the system may reduce the amount of light within the housing.

[0103] In step 1504, the system may cause the spectral sensor to capture first sensor data for the plant pole. For example, the plant pole may rotate at least 360 degrees while the spectral sensor is operating so that the spectral sensor can capture a full view of all plants, planting containers, etc. associated with the plant pole while the lighting within the enclosure is reduced.

[0104] In step 1506, the system may determine baseline reflectance data for at least one plant associated with a planting container in the planting pole. For example, the system may determine regions of interest associated with individual plants based on their known placement in the planting pole and / or planting container, or, for example, through segmentation and / or classification of sensor data.

[0105] In step 1508, the system may interface with an illuminator. For example, the illuminator may be associated with or configured to output lighting at specific or known characteristics. In some cases, the lighting may be directed toward a desired plant or area of ​​interest. In other cases, the illuminator may be directed generally toward the planting pole.

[0106] In step 1510, the system may engage or re-engage the spectral sensor to capture second sensor data for the planting pole. For example, the planting pole may again rotate at least 360 degrees while the spectral sensor is activated so that the spectral sensor captures a complete view of all plants, planting containers, etc. associated with the planting pole. However, in this example, the second sensor data represents the planting pole and plants while the lighting device is activated or enabled.

[0107] In step 1512, the system may determine reflected response data for at least one plant based at least in part on the second sensor data. For example, the system may determine the reflected response data based on known placement in the planting pole and / or planting container, or using the same region of interest associated with individual plants, for example, through segmentation and / or classification of the sensor data.

[0108] In step 1514, the system may determine resultant reflectance data based at least in part on the baseline reflectance data and the reflective response data. For example, the resultant reflectance data may represent a difference between the baseline reflectance data and the reflective response data.

[0109] In step 1516, the system may determine at least one characteristic of the at least one plant based at least in part on the resulting reflectance data. For example, the system may use the resulting reflectance data to determine the health and / or life stage of the plant by comparing the resulting data to historical and / or expected reflectance data (in some cases, expected reflectance data based on the type of plant).

[0110] 16 is another flow diagram showing an example of an illustrated process 1600 for eliciting a shade avoidance response from individual plants, according to some implementations. In some cases, lighting systems, such as the lighting and control posts described above, can trigger a shade avoidance response in individual plants, thereby promoting increased or otherwise accelerated growth.

[0111] In step 1602, the system may cause one or more illuminators associated with the housing to provide illumination to a first region of interest associated with a first plant in the planting pole. As described above, the region of interest may be associated with the first plant (as defined by classification and segmentation of sensor data that suppresses the first plant) and / or may be associated with a particular planting container.

[0112] In step 1604, the system may determine that a first time period has elapsed. For example, an illuminator may provide illumination to a first region of interest for a first period of time at a desired illumination setting (e.g., wavelength, intensity, etc.).

[0113] In step 1606, the system may adjust the position of the planting pole and / or the first region of interest to shade at least a first portion of the first plant. For example, the system may rotate, tilt, or otherwise adjust the planting pole after the end of the first time period. Alternatively, the system may adjust the first region of interest by adjusting an illumination area associated with one or more illuminators. In some cases, the adjustment may be configured to cause at least partial shading of the first plant. In one example, the system may utilize sensor data captured during the first time period and / or during a transition period between the first time period and a subsequent second time period to determine the amount of shading caused by adjusting the illumination area, the region of interest, and / or the position of the planting pole. In this example, the system may complete the adjustment in response to detecting a desired amount or percentage of shading (e.g., a desired portion of the plant is shaded by more than a threshold amount of the plant, a desired feature such as a leaf is shaded, etc.).

[0114] In step 1608, the system may cause one or more illuminators to provide illumination to the first region of interest (e.g., the adjusted region) for a duration associated with the second time period. In some cases, the system may adjust one or more features or characteristics or lighting settings provided within the second time period, as described above. In this example, the illumination provided to the first plant during the first time period may differ from the illumination provided to the first plant during the second time period. In some examples, the length or duration of the first and second time periods may also vary.

[0115] In this example, by shading a portion of the first plant, the system may cause or elicit a shade avoidance response in the plant, which may result in accelerated and / or increased growth. In some cases, by shading a desired characteristic in the first plant, the system may cause the first plant to grow in a desired manner, position, and / or direction.

[0116] In step 1610, the system may readjust the position of the planting pole and / or the first region of interest to shade at least a second portion of the first plant. Again, the system may rotate, tilt, or otherwise adjust the planting pole after expiration of the first period. Alternatively, the system may adjust the first region of interest by adjusting an illumination area associated with one or more illuminators. In some cases, this adjustment may be configured to cause partial shading of at least the first plant. In one example, the system may utilize sensor data captured during the second period and / or during a transition period between the second period and a subsequent third period to determine the amount of shading resulting from adjusting the illumination area, the region of interest, and / or the position of the planting pole. In this example, the system may complete the adjustment in response to detecting a desired amount or percentage of shading (e.g., a desired portion of the plant is shaded by more than a threshold amount of the plant, an additional desired feature such as a second leaf is shaded, etc.). In this example, the amount of shading after the second adjustment may differ from the desired amount of shading associated with the first adjustment in step 1606.

[0117] In step 1612, the system may cause one or more illuminators to provide illumination to the first region of interest (e.g., the realigned region, etc.) for a duration associated with the third time period. In some cases, the system may adjust one or more features or characteristics or lighting settings provided within the second time period, as described above. In this example, the illumination provided to the first plant during the third time period may differ from the illumination provided to the first plant during the first time period and / or the second time period. In some examples, the length or duration of the third time period, the second time period, and / or the first time period may also vary.

[0118] In this example, by shading a second portion of the first plant, the system may further cause or induce a shade avoidance response in the first plant, which may cause accelerated and / or increased growth. In some cases, by shading a desired feature in the first plant in a desired rotation or pattern, the system may cause the first plant to grow in a desired manner, position, and / or direction.

[0119] In step 1614, the system may determine that a third time period has elapsed, and in step 1616, the system may cause one or more illuminators to provide illumination to a second region of interest associated with a second plant in the planting pole. For example, the system may determine that the illumination provided during the first period, second period, and third period is sufficient for the first plant based on health, user preference, maturity, size, etc., and continue to provide illumination to other plants (e.g., a second plant) in the enclosure.

[0120] In process 1600, the system provides illumination to a first plant in three stages. However, it should be understood that the number of stages, duration, etc., can vary based on the presence or absence of a user, the capacity and utilization of the enclosure and / or planting pole, the density of the plants, plant characteristics (e.g., health, size, maturity, etc.), and the functionality of the enclosure (e.g., number of illuminators, etc.). FIG. 17 is another example illustrating a system 1700 according to some implementations. For example, in some cases, the system may be an enclosure for growing plants. In some cases, the enclosure may include mechanical systems, such as a rotatable planting pole, environmental control systems, and access doors or compartments for accessing an internal space defined by the enclosure.

[0121] The system 1700 may include one or more illuminators 1702. The illuminators 1702 may be mounted through the interior of the enclosure to provide illumination to one or more plants within the enclosure. In some cases, the illuminators 1702 may be positioned along a lighting and control pole, as described above. The illuminators 1702 may include, but are not limited to, visible light, infrared illuminators, and ultraviolet illuminators. In some cases, the illuminators may have an adjustable illumination area. In these cases, the illuminators 1702 may include angle of view shifting, tilting, magnification, and / or other adjustable features. The illuminators 1702 may also have adjustable intensity and wavelength.

[0122] The system 1700 may also include one or more sensors 1704. The sensor system 1704 may include imaging devices, spectral sensors, lidar systems, depth sensors, thermal sensors, infrared sensors, or other sensors capable of generating data representative of the physical environment. For example, the sensors 1704 may be positioned within an enclosure or associated with lighting and control poles to capture multiple frames of data from various perspectives. As described above, the sensors 1704 may be of various sizes and qualities; for example, the sensors 1704 may include imaging components that may include one or more wide screen cameras, 3D cameras, high definition cameras, and video cameras, among other types of cameras.

[0123] System 1700 may also include one or more communication interfaces 1706 configured to facilitate communication between one or more networks, one or more cloud-based systems, and / or one or more mobile or user devices. In some cases, communication interface 1706 may be configured to transmit and receive data associated with the housing. Communication interface 1706 may enable Wi-Fi-based communication, such as via frequencies defined by IEEE 802.11 standards, 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), or any suitable wired or wireless communication protocol that enables each computing device to interface with other computing devices.

[0124] In the depicted example, system 1700 also includes an input and / or output interface 1708, such as a projector, a virtual environment display, a traditional 2D display, buttons, knobs, and / or other input / output interfaces. For example, in one example, interface 1708 may include a flat display surface, such as a touch screen or LED display, configured to enable a user of system 1700 to input settings or user preferences as well as consume content associated with the housing (such as plant health updates, harvesting reminders, and recipe suggestions).

[0125] System 1700 may also include one or more processors 1710, such as at least one or more access components, control logic circuits, central processing units, or processors, as well as one or more computer-readable media 1712 for performing functions associated with the virtual environment. Additionally, each of processors 1710 may itself include one or more processors or processing cores.

[0126] Depending on the configuration, computer-readable medium 1712 may be an example of tangible, non-transitory computer storage and may include volatile and nonvolatile memory, and / or removable and non-removable media implemented in any type of technology for storing information, such as computer-readable instructions or modules, data structures, program modules, or other data. Such computer-readable media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other computer-readable media technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, solid-state storage, magnetic disk storage, RAID storage systems, storage arrays, network-attached storage, storage area networks, cloud storage, or any other medium that can be used to store information and that is accessible by processor 1710.

[0127] Some modules, such as instructions and data stores, may be stored in a computer-readable medium 1712 and configured to execute on the processor 1710. For example, as shown, the computer-readable medium 1712 may store plant detection instructions 1714, illumination instructions 1716, watering instructions 1718, notification instructions 1720, plant monitoring instructions 1722, harvesting instructions 1724, setting determining instructions 1726, and other instructions 1728. The computer-readable medium 1712 may also store data such as sensor data 1730, user settings 1732, system settings 1734, plant data 1736, model data 1738 (such as machine learning models and physics models of the planting pole and / or enclosure), and environmental data 1740 (e.g., both the interior and exterior of the enclosure).

[0128] The plant detection instructions 1714 may be configured to utilize the sensor data 1730 to detect insertion events associated with one or more seed cartridges and to detect plants as the system 1700 scans before providing customized lighting. In some cases, the plant detection instructions 1714 may be configured to determine the type and / or size of the plant via one or more machine learning models that may segment and classify the sensor data.

[0129] The lighting instructions 1716 may be configured to select a planting container and / or plant within the enclosure and provide lighting in a customized setting, as described herein. For example, the lighting instructions may cause one or more illuminators 1702 to provide an illumination field directed toward a particular area of ​​interest, plant, and / or planting container.

[0130] Watering instructions 1718 may be configured to control the amount of water and / or humidity provided to the plants within the planting poles. In some cases, watering instructions 1718 may be provided to the entire system 1700, while in other cases, watering instructions 1718 may provide customized water to each individual plant and / or planting container, in a manner similar to that described with respect to lighting.

[0131] The notification instructions 1720 may be configured to provide notifications and / or alerts to an owner or user of the system 1700. For example, the notification instructions 1720 may be configured to provide notifications and / or alerts to an owner or user of the system 1700 via a communication interface Notifications may be sent to user devices associated with system 1700 via 1706. In some cases, notifications may include harvest alerts, health alerts, setting change alerts, and the like.

[0132] The plant monitoring instructions 1722 may be configured to monitor the health, size, and / or life stage of the plants as they mature within the enclosure. For example, the plant monitoring instructions 1722 may utilize the sensor data 1730, the model data 1738, and / or the environmental data 1740 to generate plant data 1736 associated with one or more statuses or historical statuses of the plants within the enclosure.

[0133] Harvest instructions 1724 may be configured to determine harvest times or harvest windows for plants in the enclosure. For example, harvest instructions 1724 may determine that a particular plant is ready for harvest based on plant data, sensor data, and / or one or more thresholds (such as a total size threshold, a leaf size threshold, and a time period threshold) and cause notification instructions 1720 to send an alert to a user.

[0134] The setting determination instructions 1726 may be configured to determine one or more settings associated with the housing. For example, the setting determination instructions 1726 may determine lighting settings, such as intensity, length of time, and wavelength to provide to individual plants, as described above.

[0135] 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 illustrated forms of implementing the claims.

Claims

1. receiving first sensor data from a first sensor, the first sensor data representing a first plant associated with an enclosure, the enclosure configured to provide a controlled physical environment; determining a first region of interest associated with the first plant based at least in part on the first sensor data; determining at least one first characteristic associated with the first plant based at least in part on the first sensor data; determining at least one first lighting setting based at least in part on the at least one first characteristic; causing an illuminator to provide first illumination to the first region of interest associated with the first plant for a first time period based at least in part on the first lighting setting; adjusting the first region of interest by adjusting an illumination area associated with the illuminator after the first period of time has elapsed to provide at least partial shade to the first plant; causing the illuminator to provide second illumination to the adjusted first region of interest associated with the first plant for a second time period based at least in part on the first lighting setting; A method comprising:

2. The first feature is the health status of the first plant; a life stage of the first plant; The size of the first plant; the classification or type of the first plant; 2. The method of claim 1, comprising one or more of:

3. the housing includes a planting pole, the planting pole including two or more planting containers and configured to rotate about a vertical axis; The first sensor data represents at least a portion of the planting pole.

3. The method according to claim 1 or 2.

4. 4. The method of claim 1, further comprising the step of deactivating the illuminator while capturing the first sensor data.

5. 5. The method of claim 1, wherein the first illumination setting is at least one of intensity, wavelength, duration, or illuminated area.

6. determining a second region of interest associated with a second plant based at least in part on the first sensor data; determining at least one second characteristic associated with the second plant based at least in part on the first sensor data; determining at least one second lighting setting based at least in part on the at least one second characteristic; causing the illuminator to provide second lighting to the second plant based at least in part on the second lighting setting, the second lighting being different from the first lighting and having a different illumination area than the first lighting; 6. The method of claim 1, further comprising:

7. 7. The method of claim 1, wherein the illuminator is configured to move, tilt, and magnify the illuminated area.

8. receiving second sensor data from a second sensor of the housing; Furthermore, Determining the first region of interest is based at least in part on the second sensor data, a first distance between the first sensor and the illuminator, and a second distance between the second sensor and the illuminator.

8. A method according to any one of claims 1 to 7.

9. A computer program product comprising coded instructions which, when executed on a computer, implements the method according to any one of claims 1 to 8.

10. one or more processors; one or more non-transitory computer-readable media communicatively coupled to the one or more processors; Equipped with 10. A system, wherein the one or more processors are configured to implement the method of claim 1.

11. The system of claim 10, wherein the system is physically remote from the enclosure.

12. the one or more processors: receiving second sensor data associated with the housing, the second sensor data being received before the first sensor data; determining an insertion event associated with the seed cartridge based at least in part on the second sensor data; and determining a seed receptacle of a planting post associated with the housing associated with the seed cartridge based at least in part on the second sensor data; and configured to:

12. The system of claim 10 or 11, wherein the step of determining the first region of interest is performed at least in part based on the seed container.

13. the one or more processors: determining a marker associated with the housing based at least in part on the first sensor data; configured to:

13. The system of claim 10, wherein determining the first region of interest is based at least in part on a relative position of the marker with respect to the first plant.

14. the one or more processors: determining that the first plant is harvestable based at least in part on the first characteristic; 14. A system according to any one of claims 10 to 13, characterized in that it is configured to:

15. One or more communication interfaces Furthermore, 15. The system of claim 10, wherein the one or more processors are configured to send a message associated with the first plant to a user device associated with the housing.

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