Method and apparatus for autonomous indoor farming

Abstract

To provide an autonomous farming system.SOLUTION: An autonomous farming system includes a computing device that is configured to obtain plant characteristic data that characterizes one or more plant characteristics of a plant growing in at least one growing module. The computing device is further configured to determine at least one growing deficiency of the plant based on the plant characteristic data using a farming engine and to send at least one farming action to a farming controller operatively coupled to the at least one growing module. The at least one farming action includes a remedial action to improve growing conditions of the plant.SELECTED DRAWING: Figure 1A

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Application No. 62 / 934,942, filed November 13, 2019, the entire disclosure of which is incorporated herein by reference.

[0002] The present disclosure relates generally to indoor farming systems, and more particularly to methods and apparatus for autonomous indoor farming systems. [Background technology]

[0003] The world's food production system needs to solve important challenges in the coming decades. It is crucial to find ways to feed the growing world population while reducing the impact of agricultural activities on the environment. Controlled environment agriculture (CEA), including greenhouse and indoor agriculture, offers a viable alternative to traditional production for some crops. Indoor vertical agriculture allows for faster and more controlled production, regardless of the season. Indoor vertical agriculture is also not affected by other environmental variables such as pests, pollution, heavy metals, and pathogens. Indoor vertical agriculture can also reduce the impact on the environment by eliminating nutrient losses, reducing land requirements, improving waste management, reducing production losses, reducing transportation costs, and reducing clean water usage. Indoor vertical agriculture can therefore help address important challenges.

[0004] However, current methods and systems of indoor vertical farming are relatively expensive to implement and do not efficiently utilize the available space within a room or container for growing crops. For example, to implement an indoor farming system, an enclosed room or container must be provided and then configured to grow crops or plants in a controllable environment. Environmental parameters such as lighting, temperature, humidity, and airflow can be controlled within the room or container to achieve the benefits of indoor farming discussed above. However, such environmental control requires relatively expensive sensor and control systems. Furthermore, shelves and / or racks for maintaining the plants must be placed within the room or container, and for relatively large rooms or containers, space is allocated within the room or container to allow a human operator to walk inside the enclosed room or container to access each shelf and / or rack. Thus, much of the space within the room or container is not allocated for growing plants, but instead is allocated to allow human access and movement within the room or container. This is an inefficient utilization of expensive space within the enclosed room or container for growing plants / crops.

[0005] Furthermore, current methods and systems of indoor vertical farming cannot operate autonomously. Current methods and systems cannot evaluate the condition of the growing plants and adjust the environment in the growing area to best provide what the growing plants need based on the condition of the plants. Current methods and systems cannot learn from previous growing cycles to modify and adjust the environment in the growing area to optimize the conditions for the growing plants in the growing area. Current methods and systems of indoor vertical farming cannot learn from previous growing cycles to automatically adjust the environment in the growing area in response to the condition and health of the plants growing in the growing area. Thus, current indoor farming methods and systems are not entirely satisfactory. Summary of the Invention [Means for solving the problem]

[0006] This section provides a general summary of the disclosure and is not an exhaustive disclosure of its entire scope or all of its features.

[0007] In some embodiments according to the present disclosure, an autonomous agricultural system can include a computing device configured to acquire plant characteristic data characterizing one or more plant characteristics of a plant growing in at least one growing module and determine, using an agricultural engine, at least one growth deficiency of the plant based on the plant characteristic data. The computing device can also be configured to transmit at least one agricultural action to an agricultural controller operatively coupled to the at least one growing module, where the at least one agricultural action includes a remedial action to improve growing conditions of the plant.

[0008] In one aspect, the agriculture engine can include a trained machine learning model. In other embodiments, the plant characteristic data can be collected by one or more sensors located on at least one of the growth modules.

[0009] In other embodiments, the computing device can be configured to acquire plant environment data characterizing growing conditions of a plant within the at least one growth module, and at least one growth deficiency can be determined based on the plant environment data.

[0010] In other aspects, the agriculture engine can include a machine learning model trained using the historical plant characteristic data and the historical environmental data.

[0011] In other aspects, the agriculture controller can be operatively coupled to at least one of an air circulation system, a lighting system, an irrigation system, a vision system, and a liquid circulation system.

[0012] In other embodiments, the at least one agricultural action can include instructions to cause at least one of an air circulation system, a lighting system, an irrigation system, a vision system, and a liquid circulation system to change growing conditions in the growing module.

[0013] In other aspects, the plant characteristic data can include image data collected by a vision system in the growing module, where the image data can include an image of the plant in the growing module.

[0014] In other embodiments, the agriculture engine can automatically determine at least one deficiency of the plant based on the image history data.

[0015] In other aspects, the computing device may be configured to retrain a machine learning model of the agricultural engine and replace the initial machine learning model with the retrained machine learning model if the computing device determines that the performance of the retrained machine learning model surpasses the performance of the initial machine learning model.

[0016] In another embodiment, the at least one growing module includes a plurality of growing containers arranged in at least two columns and at least two rows to define a matrix of growing containers, and the computing device is capable of acquiring plant characteristic data characterizing one or more plant characteristics of a plant growing in each of the plurality of growing containers.

[0017] In some embodiments according to the present disclosure, an autonomous method is provided. The method of autonomous agriculture includes acquiring plant characteristic data characterizing one or more plant characteristics of a plant growing in at least one growing module, and cultivating the plant based on the plant characteristic data using an agriculture engine. The method may include determining at least one growth deficiency of the object. The method may also include transmitting at least one agricultural action to an agricultural controller operably coupled to the at least one growth module, where the at least one agricultural action includes a remedial action for improving growing conditions of the plant.

[0018] In some embodiments according to the present disclosure, the autonomous agricultural device may include a plurality of enclosed growing containers each having an independently controllable environment, where the plurality of enclosed growing containers are oriented into at least two columns and at least two rows to define a matrix of growing containers. The autonomous agricultural device may also include at least one movable cart located at each of the plurality of enclosed growing containers, where each of the at least one movable cart is configured to support one or more plants. The articulated robot may be movably positioned on the track and the riser such that the articulated robot may move in a first direction along the track to access each row of the matrix of growing containers and may move in a second direction along the riser to access each row of the matrix of growing containers.

[0019] In another embodiment, the articulated robot is capable of extracting and inserting at least one movable cart from each growing container in the matrix of growing containers.

[0020] In other embodiments, each growing container in the matrix of growing containers can include at least two tiers configured to support at least one movable cart at different positions relative to the bottom of the growing container.

[0021] In other embodiments, the autonomous agricultural device may include a computing device and an agricultural controller. The computing device may be connected to the agricultural controller and may be configured to determine at least one deficiency of the one or more plants using the trained machine learning model.

[0022] In other embodiments, the computing device can determine at least one deficiency based on plant characteristic data obtained from one or more sensors located in each growing module.

[0023] In other embodiments, the computing device can determine at least one deficiency further based on environmental data obtained from one or more sensors located on each growth module.

[0024] In other aspects, the machine learning model can be trained using historical plant characteristic data and historical environmental data obtained from sensors located in each growth module.

[0025] In another aspect, the computing device can transmit agricultural actions to one of an air circulation system, a lighting system, an irrigation system, a vision system, and a liquid circulation system located at one of the plurality of growing modules, such that one of the air circulation system, the lighting system, the irrigation system, the vision system, and the liquid circulation system changes growing conditions at one of the plurality of growing modules.

[0026] Further areas of applicability will become apparent from the description provided herein.The description and specific examples in this summary are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.

[0027] Aspects of the present invention are best understood from the following detailed description when read in conjunction with the accompanying drawing figures. The various features are not necessarily drawn to scale, and in fact the dimensions and geometries of the various features may be arbitrarily increased or decreased for clarity of illustration. [Brief description of the drawings]

[0028] [Figure 1A] FIG. 1 illustrates a perspective view of an autonomous indoor farming facility, according to some embodiments. [Figure 1B] FIG. 1 illustrates a perspective view of an autonomous indoor farming facility, according to some embodiments. [Figure 1C] FIG. 1 illustrates a perspective view of an autonomous indoor farming facility, according to some embodiments. [Figure 1D] FIG. 1 illustrates a perspective view of an autonomous indoor farming facility, according to some embodiments. [Diagram 2] FIG. 1 illustrates a perspective view of an indoor farming module, according to some embodiments. [Diagram 3] FIG. 1 illustrates an example perspective view of an indoor farming module with one end open to reveal multiple vertical layers of plant trays, each having multiple rows of plant trays, spanning the entire interior width of the module, according to some embodiments. [Figure 4A] FIG. 1 illustrates an exemplary end view of an indoor farming module, according to some embodiments. [Figure 4B] FIG. 1 illustrates an exemplary end view of an indoor farming module, according to some embodiments. [Figure 4C] FIG. 1 illustrates an exemplary end view of an indoor farming module, according to some embodiments. [Diagram 5] FIG. 1 illustrates a perspective view of an autonomous indoor farming facility, according to some embodiments. [Figure 6A] FIG. 1 illustrates a system block diagram of an autonomous indoor farming system, according to some embodiments. [Figure 6B] FIG. 1 illustrates an example block diagram of a controller in an indoor farming facility, according to some embodiments. [Figure 7] FIG. 1 illustrates a block diagram of an artificial intelligence (AI) system for autonomous indoor farming, according to some embodiments. [Figure 8]6B illustrates a block diagram of an aspect of the autonomous farming system of FIG. 6A, according to some embodiments. [Figure 9] 1 is an example of a plant and an exemplary indicator of a deficiency, according to some embodiments. [Figure 10] 1 is an example of a fruit and an exemplary indicator of deficiency, according to some embodiments. [Figure 11] 1 is an example of another plant, as well as an exemplary indicator of a deficiency, according to some embodiments. [Figure 12] 1 is an example of yet another plant and an exemplary indicator of a deficiency, according to some embodiments. [Figure 13] 1 is an example of another plant, as well as an illustrative indicator of a deficiency showing its progression over time, according to some embodiments. [Figure 14] 1 is a flowchart illustrating an example method for autonomous farming, according to some embodiments. [Figure 15] 1 is a flowchart illustrating another example method of autonomous farming, according to some embodiments. [Figure 16] 1 is a flowchart illustrating an example method for retraining a machine learning model, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] Various exemplary embodiments of the present invention are described below with reference to the accompanying drawings to enable one skilled in the art to make and use the present invention. It will be apparent to one skilled in the art that, after reading this disclosure, various changes or modifications can be made to the examples described herein without departing from the scope of the present invention. Thus, the present invention is not limited to the exemplary embodiments and applications described or illustrated herein. Additionally, the specific order or hierarchy of steps in the methods disclosed herein are merely exemplary approaches. Based on design preferences, the present invention may be modified in various ways. However, the specific order or hierarchy of steps in a disclosed method or process may be rearranged while remaining within the scope of the invention. Thus, those skilled in the art will understand that the methods and techniques disclosed herein present the various steps or acts in a sample order, and that, unless expressly stated otherwise, the invention is not limited to the specific order or hierarchy presented. It will be.

[0030] The embodiments of the present invention will be described in detail with reference to the accompanying drawings. The same or similar components may be designated by the same or similar reference numerals even if they are shown in different drawings. Specific examples of components and arrangements are described below to simplify the disclosure. Of course, these are illustrative and not intended to be limiting. For example, when an element is referred to as being "coupled" or "connected" to another element, it will be understood that it may be directly coupled or connected to the other element, or there may be one or more intervening elements.

[0031] 1A-1D show perspective views of an autonomous indoor farming facility 100 according to some embodiments. In the illustrated embodiment, the autonomous indoor farming facility 100 includes ten indoor farming modules 102 arranged in five rows, with each row including two stacked indoor farming modules 102. In the illustrated embodiment, the autonomous indoor farming facility 100 includes at least one tray handling system designed to automatically load and unload carts through a first end of the indoor farming modules 102. In some other embodiments, the tray handling system is further designed to automatically plant and remove individual crops from the carts.

[0032] In an exemplary embodiment, the tray handling system includes the articulated robot 104, two linear transport systems 106 / 108, a track 110, and a robot controller (not shown). In some embodiments, the tray handling system is configured to transfer a given cart from the chassis of the indoor agriculture module 102 to a given location (e.g., a storage rack). In some embodiments, when inserting a new crop into the indoor agriculture module 102, the tray handling system is configured to transfer a given cart from the storage rack to the chassis of the indoor agriculture module 102.

[0033] In some embodiments, the indoor agriculture module 102 includes at least one of the following subsystems to provide a controllable environment for the crops within the indoor agriculture module 102: an air circulation system, a lighting system, an irrigation system, a liquid circulation system, a vision system, a controller, and a local computer. Each of the at least one subsystem in the indoor agriculture module 102 is described in detail below.

[0034] In some embodiments, the articulated robot 104 is configured external to the indoor farming modules 102 in the autonomous indoor farming facility 100. In some embodiments, the articulated robot 104 includes multiple joints for controlling multiple arm segments coupled to corresponding joints. In some embodiments, each of the multiple joints is coupled to an electric motor (not shown) to provide rotational motion to each of the multiple joints. In some embodiments, the articulated robot 104 has multiple axes that enable the articulated robot 104 to approach and precisely load and unload carts at the multiple indoor farming modules 102. In some embodiments, the articulated robot 104 includes a fork-type attachment for releasably coupling to a cart during transport, which will be described in more detail below.

[0035] In some embodiments, the robot controller is configured to manage and operate the tray handling system according to predetermined rules or pre-scheduled operations. In some embodiments, the predetermined rules or pre-scheduled operations are determined by the growth state and growth step of the plants in the trays on the cart, where the growth state and growth step are monitored in real time via a vision system or predicted by a machine learning model as described in more detail below. In some embodiments, the second robot 112 can be configured adjacent to the articulated robot 104 or the storage rack 114 / 116 to remove the trays from the cart so that the plants in the trays can be harvested and the cart can be reused. In some embodiments, the robot controller can move a given tray to a predetermined position. The robot controller includes a motion planning unit that is used to determine the trajectory of the robot to move the cart to a desired position and to register any position changes of other trays during the course of the transfer of a given tray. In some embodiments, the robot controller is coupled to a cloud computer that further receives instructions from a controller at the indoor agriculture module 102 and / or a remote control station. For example, upon confirmation that a cart has been unloaded from the indoor agriculture module 102, the controller of the indoor agriculture module 102 may pause the irrigation schedule, rotate the plurality of liquid distribution pipe assemblies, open the roll-up door 120 at the first end 130 of the indoor agriculture module 102, and then activate the articulated robot 104.

[0036] As shown in FIG. 1A, the agricultural facility 100 can include a number of agricultural modules 102 arranged in multiple rows and multiple columns. In the illustrated example, the agricultural facility includes ten agricultural modules 102, with five agricultural modules located at one level in a first row and five additional agricultural modules 102 located in a second row at the top end of the first row. In such an arrangement, the agricultural modules define a matrix of agricultural modules or a matrix of growing containers 102. The robot 104 is located on a track 110 and a riser 108 such that the robot 104 can move to any desired position relative to the matrix of agricultural modules 102. The support platform 106 can move vertically along the riser 108, which can move along the track 110. In this manner, the robot 104 can move and / or be articulated to each agricultural module 102 in the matrix to access plants in the agricultural modules in any desired row and column of the matrix. In other examples, the matrix can have different sizes and can include more than two rows and five columns. In yet other instances, the matrix may have fewer than five columns.

[0037] In some embodiments, each of the plurality of indoor agriculture modules 102 can be accessed from a second end 140, where the second end 140 comprises an access door for maintenance purposes. In some embodiments, a staircase 122 is further configured at the second end 140 of the indoor agriculture modules 102 to provide access to the stacked indoor agriculture modules 102.

[0038] FIG. 2 illustrates a perspective view of a container or enclosure 200 that may be utilized to provide an indoor farming module according to some embodiments. In some embodiments, the container 200 is a standard shipping container used to transport goods across oceans, typically loaded onto cargo tankers and then loaded via "18-wheeler" trucks for transport to land. Such standard shipping containers are ubiquitous today, and used shipping containers can be purchased relatively inexpensively. Thus, relatively inexpensive "off the shelf" containers can be modified into indoor farming modules 200 according to various embodiments of the present invention, as described in more detail below. Standard shipping containers 200 are typically constructed of steel and are fully enclosed. As shown in FIG. 2, a typical shipping container 200 is in the form of an elongated rectangular box having a roof 202, a bottom 206, two side walls 204, a front wall 230, and a back wall 240. In some embodiments, a double wall (not shown) is provided on the front wall 230 to provide access to crops or plants (collectively hereinafter "crop") that may be accessed (e.g., loaded, handled, inspected, processed, etc.) within the container 200. Various types of doors (e.g., single, double, garage-type doors, rolling-type doors, etc.) may be installed on the front wall 230 as desired, or existing doors on the standard shipping container 200 may be used. Similarly, the rear wall 240 may be modified with various types of doors to provide operator access to the environmental control systems and equipment located in the rear region of the container 200.

[0039] A standard shipping container 200 generally has a length L of 40 feet, a height H of 9 feet 6 inches, and a width W of 8 feet. The inventors have determined that the dimensions of a standard shipping container 200 are It has been found that the present invention provides a cost-effective approach to indoor agriculture, especially when crops are filled to capacity and environmental control for continuous year-round agriculture is provided. However, in alternative embodiments, it is understood that the present invention is not limited to modifying standard shipping containers to provide indoor agriculture modules. Other types of enclosures or containers having the same or different dimensions and made of the same or different materials can be utilized based on the inventive principles disclosed herein according to various alternative embodiments of the present invention.

[0040] FIG. 3 illustrates an exemplary perspective view of an indoor farming module 300 with the front wall 230 opened or removed to show the interior compartment of the container 200 according to some embodiments of the present invention. In the illustrated embodiment, for clarity of explanation, the indoor farming module 300 omits the air circulation system, the liquid circulation system, and the lighting system, which are described in more detail below separately. As shown in FIG. 3, the indoor farming module 300 includes an interior compartment 302. In some embodiments, the module 300 is a standard refrigerated shipping container that has been modified for indoor farming, as described in more detail below. In the illustrated embodiment, the indoor farming module 300 includes a chassis 304. In some embodiments, the chassis 304 includes a plurality of vertical frames and a plurality of horizontal frames. In the illustrated embodiment, the chassis 304 includes four stages, namely a first stage 306-1, a second stage 306-2, a third stage 306-3, and a fourth stage 306-4. Each of the four steps 306 of the chassis 304 extends in a first direction (i.e., the x-direction) from a first wall 308-1 to a second wall 308-2 of the interior compartment 302. In some embodiments, the first wall 308-1 and the second wall 308-2 are side walls along a long side of the interior compartment 302.

[0041] Each of the four stages 306 of the chassis 304 includes three sets of guide rails 310 extending along a second direction (i.e., the y direction) perpendicular to the first direction (i.e., the x direction). In some embodiments, the three sets of guide rails 310 are configured parallel and side-by-side such that the three sets of guide rails in a stage 306 occupy the entire width of the container compartment 302 (i.e., the space between the first wall 308-1 and the second wall 308-2).

[0042] In an exemplary embodiment, a plurality of carts 312 with wheels sized and spaced to rotate on a set of guide rails 310 are provided on the chassis 304. Each of the plurality of carts 312 is detachably coupled to an adjacent cart 312 on the same set of guide rails 310 via a cart coupler (not shown). In some embodiments, the cart coupler is configured to couple and fix to an articulated robot during transportation. Each of the plurality of carts 312 in the indoor farming module 300 is configured to carry a tray including a seed pot (not shown) in which a plurality of plants are planted. In some embodiments, the seed pot includes a plurality of holes, and the arrangement of the holes is determined by the growing conditions of the plurality of plants. In some embodiments, each of the three sets of guide rails 310 can carry eight carts, and the indoor farming module 300 can carry up to 96 carts.

[0043] The lighting and water circulation assemblies are mounted to a plurality of horizontal frames of the chassis 304 to provide light illumination and liquid supply to each of the trays in the corresponding cart, according to some embodiments, as described in more detail below in Figures 4A-4C. In some embodiments, the indoor farming module 300 controls the growing conditions (e.g., humidity, temperature, CO2) within a completely enclosed environment. 2The interior compartment 302 is fully closed with a roll-up door (not shown) at the first end to maintain the height (e.g., level) of the cart. The roll-up door is opened, for example, when the cart is transported in or out of the interior compartment 302. It is noted that the indoor farming module 300 shown in FIG. 3 is merely exemplary and is not intended to be limiting of the present invention. Thus, it is understood that the chassis in the indoor farming module 300 of FIG. 3 can be configured with any number of steps 306, any number of guide rails 310, and can carry any number of carts 312 according to various embodiments of the present invention.

[0044] 4A-4C show an example end view of an indoor farming module 400 with an air circulation system, a water circulation system, a lighting system, and a control system according to some embodiments of the present invention. It is noted that the indoor farming module 400 shown in FIGS. 4A-4C is merely exemplary and is not intended to limit the present invention. Thus, it is understood that additional functional blocks may be provided or combined in the indoor farming module 400 of FIGS. 4A-4C and / or some other functional blocks may be omitted.

[0045] In the illustrated embodiment, the indoor farming module 400 includes a chassis 304 having four steps in the container compartment 302, namely a first step 306-1, a second step 306-2, a third step 306-3, and a fourth step 306-4. In some embodiments, the chassis 304 includes a plurality of vertical frames and a plurality of horizontal frames. Each of the four steps 306 of the chassis 304 extends from a first wall 308-1 in a first direction (i.e., the x-direction) to a second wall 308-2 of the indoor farming module 400. Each of the four steps 306 of the chassis 304 includes three sets of guide rails 310 extending along a second direction perpendicular to the first direction. The three sets of guide rails 310 are configured alongside each other such that the three sets of guide rails of the steps 306 traverse the entire width of the interior compartment 302 in the first direction.

[0046] Each of the plurality of carts 312 in the indoor agriculture module is configured to carry a tray including a seed pot 402 in which a plurality of plants 404 are planted. In some embodiments, the seed pot 402 includes a hole arrangement (not shown), which is determined by the growing conditions of the plurality of plants 404. In some embodiments, each of the three sets of guide rails 310 can carry eight carts, and the indoor agriculture module 400 can carry 96 carts. It is noted that the indoor agriculture module 400 is merely exemplary and is not intended to be limiting of the present invention. Thus, it is understood that the chassis 304 in the indoor agriculture module 400 of Figures 4A-4C can be configured with any number of steps 306, any number of sets of guide rails 310, and any number of carts 312 that can be appropriately sized for each configuration.

[0047] In an exemplary embodiment, each of the 96 carts in the indoor farming module 400 is provided with a lighting assembly 406 and a liquid circulation assembly 408. In an exemplary embodiment, the lighting assembly 406 and the liquid circulation assembly 408 are structurally supported on a corresponding horizontal frame above each of the plurality of carts 312 and above the floor 410. In an exemplary embodiment, the liquid circulation assembly 408 includes a liquid supply conduit, a plurality of liquid return conduits, a plurality of liquid distribution pipe assemblies, a plurality of drain conduits, and a plurality of stepper motor assemblies. Examples of such configurations are described in more detail in U.S. Patent Application No. 16 / 870,675, filed May 8, 2020, which is incorporated by reference as if fully set forth herein. In some embodiments, the lighting assembly 406 includes at least one lighting module, which is described in more detail below. In some embodiments, the plurality of drain conduits are coupled together to a drain collection conduit 412 and further connected to an external drain container.

[0048] In some embodiments, the indoor agriculture module 400 includes an air circulation system including an air distribution / air conditioning unit 424, at least one air dehumidification unit 426, and a control unit 428. In some embodiments, the air distribution / air conditioning unit 424 is coupled to a suspended ceiling. In some embodiments, the air distribution / air conditioning unit 424 is sized based on the mass of the plants and the volume of the container. In some embodiments, the air distribution / air conditioning unit 424 is used to regulate the atmosphere in the growing area of ​​the indoor agriculture module and is sized based on the heat generated by the lighting assembly. In some embodiments, the air distribution / air conditioning unit 424 generates cool, dry air that is then circulated through a recirculation fan. The air is drawn into the plenum and distributed to a suspended ceiling 420 located at the top of the growing area. In some embodiments, the suspended ceiling 420 has gaps along the left and right sides to facilitate air movement. As cool air enters the suspended ceiling 420, it is pressurized and creates a positive displacement. This displacement distributes some of the air to the lower side walls. In some embodiments, motorized dampers control the volume of the remaining air that is released from the rear of the plenum. In some embodiments, predetermined set points in the control program regulate the airflow as plant mass increases during different stages of the growing cycle. In this design, the air circulation system operates as a push-pull air exchange that absorbs heat and moisture as it returns for reconditioning.

[0049] In some embodiments, the airflow is controlled by a controller 428 that can dynamically adjust the airflow with the mass of plants as it increases during different stages of the growing cycle. As dry, cool air enters the spaces between the stages, the humidity and temperature of the airflow increases. In some embodiments, at least one air dehumidification unit 426 receives the moist air returning from the growing area and provides dry air to the air blowing / conditioning unit 424 across the suspended ceiling 424 of the interior compartment 302. Water condensed during the dehumidification process is drained into a collection reservoir (not shown) for filtration and recycling. Thus, according to various embodiments of the present invention, the air circulation system described above and illustrated in Figures 4A-4C facilitates the control of environmental parameters such as temperature, humidity, and air volume to precisely control and maintain optimal growing conditions within the interior compartment 302 depending on the types of crops being grown and their growing cycle conditions.

[0050] FIG. 5 shows a perspective view of an autonomous indoor farming facility 500 according to some embodiments. In the illustrated embodiment, the autonomous indoor farming facility 100 includes 22 indoor farming modules 102 arranged in multiple rows, some of the multiple rows including two stacked indoor farming modules 102. In the illustrated embodiment, the autonomous indoor farming facility 500 includes one tray handling system 502. In the illustrated embodiment, the tray handling system 502 includes an articulated robot, two linear transport systems, a track, and a robot controller (not shown), as described above. In some embodiments, the tray handling system 502 is configured to transfer a given cart from the chassis of the indoor farming module 102 to a given location (e.g., a storage rack). In some embodiments, when inserting a new crop into the indoor farming module 102, the tray handling system is configured to transfer a given cart from the storage rack to the chassis of the indoor farming module 102 on both sides of the track. In some other embodiments, the tray handling system 502 is further designed to automatically plant and remove individual crops from the cart. It should be noted that the embodiment shown in Figure 5 is for illustrative purposes and does not limit the scope of the present invention. There may be any number of indoor agriculture modules 102 in the autonomous indoor farming facility 500, and there may be any number of tray handling systems 502. In some other embodiments, the tray handling system 502 does not include a track, where the movement of the tray handling system 502 can be steered via one of the following technologies: wires, guide tapes, lasers, gyroscopes, and vision systems. In some embodiments, the tray handling system 502 can be powered by a battery that can be wirelessly charged.

[0051] FIG. 6A illustrates a system block diagram of an autonomous indoor farming system 600 according to some embodiments of the present invention. It is noted that the autonomous indoor farming system 600 is merely exemplary and is not intended to limit the present invention. It is therefore understood that additional functional blocks may be provided or combined in the system 600 of FIG. 6A, or that some other functional blocks may be omitted or briefly described herein. It is also noted that the functionality provided in each of the components and modules of the system 600 may be combined or separated into one or more modules.

[0052] In some embodiments, the system 600 includes multiple indoor farming facilities 602, i.e. 1 indoor agricultural facility 602-1, 2 indoor agricultural facility 602-2, 3 indoor agricultural facility 602- 6A , the fifth indoor agriculture facility 602-5 includes a third indoor agriculture facility 602-6, a fourth indoor agriculture facility 602-4, and a fifth indoor agriculture facility 602-5. In some embodiments, each of the plurality of indoor agriculture facilities 602 includes a tray handling system 604 and at least one indoor agriculture module 606. In the illustrated embodiment of FIG. 6A , the fifth indoor agriculture facility 602-5 includes eight indoor agriculture modules 606 arranged in four rows, each row including two stacked indoor agriculture modules 606. In alternative embodiments, the autonomous indoor agriculture system can be implemented in a non-modular system having a single agriculture facility with a single controlled environment zone. In some embodiments, each of the plurality of indoor agriculture facilities 602 is further coupled to a remote computer 632 via a communication network 630 (e.g., the Internet).

[0053] In some embodiments, the remote computer 632 is a mobile device. In an alternative embodiment, the remote computer 632 includes at least one server computer coupled to a database that stores environmental parameters and other data and instructions for analyzing data information provided by each subsystem of each indoor farming module 606, and then providing additional instructions for automatically monitoring and controlling the operation of the system 600 described above.

[0054] In an example embodiment, the tray handling system 604 is designed to automatically load and unload carts through a first end of the indoor agriculture module 606. In an example embodiment, the tray handling system 604 includes an articulated robot, a linear transport system, and a robot controller, as described above. In some embodiments, the tray handling system 604 is configured to transfer a given cart from a chassis of the indoor agriculture module 606 to a given location (e.g., a storage rack). In some embodiments, when inserting a new crop into the indoor agriculture module 606, the tray handling system 604 is configured to transfer a given cart from the storage rack to the chassis of the indoor agriculture module 606.

[0055] In some embodiments, the indoor agriculture module 606 includes at least one of the following subsystems: an air circulation system 610, a lighting system 612, an irrigation system 614, a liquid circulation system 618, a controller 620, and a local computer 622. In some embodiments, the liquid circulation system 618 is configured external to the indoor agriculture module 606. In some embodiments, the liquid circulation system 618 may be shared by two stacked indoor agriculture modules 606 and may be controlled by one of the controllers 620 of the indoor agriculture modules 606.

[0056] In some embodiments, the air circulation system 610 includes a blower unit, an air conditioning unit, an air dehumidification unit, and a sensor. In some embodiments, the air circulation system 610 further includes a suspended ceiling for airflow regulation. In some embodiments, the blower unit, the air conditioning unit, the air dehumidification unit, and the suspended ceiling control humidity, CO2, and other parameters for each of a plurality of plants on a plurality of carts on different levels of a chassis in the growing area of ​​the indoor agriculture module 606. 2 It is configured to provide effective regulation of level, gas mixture, air flow, and air temperature.

[0057] In some embodiments, the irrigation system 614 includes multiple liquid supply conduits, multiple liquid return conduits, multiple drain conduits, and multiple liquid distribution assemblies, as described above. In some embodiments, the irrigation system 614 further includes valves and stepper motors for controlling the position of the multiple liquid distribution assemblies. In some embodiments, the irrigation system 614 is directly coupled to the liquid circulation system 618.

[0058] In further embodiments, the liquid circulation system 618 can include a effluent reservoir, at least one filter, at least one water reservoir, at least one nutrient reservoir, a pump, a plurality of sensors, and a plurality of control units. In some embodiments, the plurality of sensors includes at least one temperature sensor, at least one pH sensor, at least one electrical conductivity (EC) sensor, and at least one dissolved oxygen (DO) sensor. In some embodiments, the plurality of control units includes at least one of a temperature control unit, a pH control unit, a dissolved oxygen control unit, and a nutrient control unit, each operably coupled to a plurality of liquid supply conduits and / or a plurality of liquid return conduits for controlling the composition and properties (e.g., temperature, pH, etc.) of the liquid flowing through the irrigation system 614. In some embodiments, the liquid circulation system 618 regulates nutrient levels, oxygen levels, pH levels, temperature, and particle levels in the irrigation liquid to support the growth of plants in the trays of the indoor agriculture module 606.

[0059] In an exemplary embodiment, the lighting system 612 includes a plurality of lighting modules, each of which includes at least one of the following photon sources: incandescent, fluorescent, halogen, high pressure sodium, plasma, and light emitting diode (LED) to provide photons for the photosynthetic response of the plants. In some embodiments, the photon source is selected according to a desired light spectrum for the plants. In some embodiments, the lighting system 612 further includes at least one power source to provide power to the plurality of lighting modules. In some embodiments, the at least one power source can adjust and control light intensity, uniformity, and light spectrum to provide desired illumination to the plants in the indoor agriculture module 606. In some embodiments, the lighting system 612 can further include a plurality of optical sensors to measure light intensity, uniformity, and light spectrum.

[0060] In some embodiments, the indoor agriculture module 606 further includes a vision system 616. In some embodiments, the vision system 616 includes at least one image sensor and at least one light source. In some embodiments, the vision system 616 is configured external to the indoor agriculture module 606. In alternative embodiments, the vision system 616 can also be configured within the indoor cultivation module 606 to monitor plant growth, as described in more detail below. The vision system 616 can also include a transport mechanism. The transport mechanism can include, for example, a robotic arm, articulated member, track, cylinder, or other movable element that can move the image sensor and / or light source to a desired location in the agriculture module 606 to capture data, images, and other information used to monitor and / or control plant conditions and growth conditions in the agriculture module 606.

[0061] FIG. 6B illustrates an example block diagram of a controller 620 in an indoor farming facility according to some embodiments of the present invention. It is noted that the controller 620 is merely exemplary and is not intended to limit the present invention. It is therefore understood that additional functional blocks may be provided or combined with the controller 620 of FIG. 6B, or some other functional blocks may be omitted or briefly described herein. It is noted that the functionality provided for each component and module of the controller 620 may be combined or separated into one or more modules.

[0062] In the illustrated embodiment, the controller 620 includes a processor 622, a memory 624, an input / output interface 626, a communication interface 628, and a system bus 634, according to some embodiments. The processor 622 may include any processing circuitry operable to control the operation and performance of the indoor agriculture modules in the indoor agriculture facility and tray handling system. In various aspects, the processor 622 may be a general purpose processor, a chip multiprocessor (CMP), a special purpose processor, an embedded processor, a digital signal processor, or a processor. The processor 622 may be implemented as a digital signal processor (DSP), a network processor, an input / output (I / O) processor, a media access control (MAC) processor, a wireless baseband processor, a coprocessor, a microprocessor such as a complex instruction set computer (CISC), a reduced instruction set computing (RISC) microprocessor, and / or a very long instruction word (VLIW) microprocessor, or other processing device. The processor 622 may also be implemented by a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic device (PLD), etc.

[0063] In various aspects, the processor 622 may be configured to execute an operating system (OS) and various applications. Examples of OS include operating systems commonly known by trade names such as Apple OS, Microsoft Windows OS, Android OS, and other proprietary or open source OS. Examples of applications include a phone application, a camera (e.g., digital camera, video camera) application, a browser application, a multimedia player application, a game application, a messaging application (e.g., email, short message, multimedia), a viewer application, and the like.

[0064] In some embodiments, at least one computer-executable program embodied therein is provided. A non-transitory computer-readable storage medium may be provided, where the computer-executable instructions, when executed by at least one processor, cause the at least one processor to perform embodiments of the methodologies described herein. The computer-readable storage medium may be embodied in memory 624.

[0065] In some embodiments, the memory 624 may include any machine-readable or computer-readable medium capable of storing data, including both volatile / non-volatile and removable / non-removable memory. The memory 624 may include at least one non-volatile memory unit. The non-volatile memory unit may store one or more software programs. The software programs may include applications, user data, device data, and / or configuration data, or combinations thereof, to name a few. The software programs may include instructions executable by various components of the robotic controller of the tray processing system 502, 606.

[0066] For example, the memory may include read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM), double data rate DRAM (DDR-RAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory (e.g., NOR or NAND flash memory), content addressable memory (CAM), polymer memory (e.g., ferroelectric polymer memory), phase change memory (e.g., ovonic memory), ferroelectric memory, silicon oxide nitride oxide silicon (SONOS) memory, disk memory (e.g., floppy disk, hard drive, optical disk, magnetic disk), card (e.g., magnetic card, optical card), or any other type of medium suitable for storing information.

[0067] In one embodiment, memory 624 may include a set of instructions in the form of a file for performing a method for generating one or more timing libraries as described herein. The set of instructions may be stored in any acceptable form of machine-readable instructions, including source code or various suitable programming languages. Some examples of programming languages ​​include Java, C, C++, C#, and Py thon, Objective-C, Visual Basic, or .NET programming In some embodiments, a compiler or interpreter is configured to convert a set of instructions into machine-executable code for execution by a processor.

[0068] In some embodiments, the I / O interface 626 may include any suitable mechanism or component that allows a user to input to and output to an indoor agriculture module of the indoor agriculture system. For example, the I / O interface 626 may include any suitable input mechanism, including, but not limited to, a button, a keypad, a keyboard, a click wheel, a touch screen, or a motion sensor. In some embodiments, the I / O interface 626 may include a capacitive sensing mechanism or a multi-touch capacitive sensing mechanism (e.g., a touch screen).

[0069] In some embodiments, the I / O interface 626 may include a visual peripheral output device to provide a display visible to a user. For example, the visual peripheral output device may include a screen, such as a liquid crystal display (LCD) screen, that is integrated into the indoor agriculture module. As another example, the visual peripheral output device may include a movable display or projection system that displays content on a surface remote from the indoor agriculture equipment. In some embodiments, the visual peripheral output device may include a coder / decoder, also known as a codec, that can convert digital media data into an analog signal. For example, the visual peripheral output device may include a video codec, an audio codec, or any other suitable type of codec.

[0070] The visual peripheral output device may also include a display driver, circuitry for driving the display driver, or both. The visual peripheral output device may be operable to display content under the direction of the processor. For example, the visual peripheral output device may be capable of playing media playback information, application screens of applications implemented in the indoor agriculture module, information regarding ongoing communication operations, information regarding incoming communication requests, or device operation screens, to name a few.

[0071] In some embodiments, the communication interface 628 may include any suitable hardware, software, or combination of hardware and software capable of coupling the indoor agriculture modules of the indoor agriculture system to one or more networks and / or additional devices. The communication interface 628 may be arranged to operate with any suitable technology for controlling information signals using a desired set of communication protocols, services, or operating procedures. The communication interface 628 may include suitable physical connectors for connecting with a corresponding communication medium, whether wired or wireless.

[0072] According to some embodiments, the communication system and method includes a network. In various aspects, the network may include, but is not limited to, a local area network (LAN), as well as a wide area network (WAN) including the Internet, wired channels, wireless channels, telephone, computer, communication devices including wired, radio, optical, or other electromagnetic channels, and other devices and / or components that may be associated with communication data, including combinations thereof. For example, the communication environment may include intrabody communication, various devices, and various communication modes such as wireless communication, wired communication, and combinations thereof.

[0073] A wireless communication mode refers to a mode that utilizes, at least in part, wireless technology including wireless transmission, data, and various protocols and combinations of protocols associated with the device. (e.g., nodes). Points include computers and computer-related devices and components, such as, for example, wireless devices, such as wireless headsets, audio and multimedia devices and machines, such as audio and multimedia players, telephones, including cellular and cordless telephones, printers, network-connected equipment, such as circuit generation systems, and / or any other suitable device or third party device.

[0074] Wired communication modes include any mode of communication between points utilizing wired technology, including wired transmission, data, and various protocols and combinations of protocols associated with devices. Points include, for example, devices such as audio and multimedia devices and machines, such as audio and multimedia players, telephones, including cellular and cordless telephones, and computers and computer-related devices and components, such as printers, network-connected appliances, and / or any other suitable devices or third-party devices. In various embodiments, the wired communication module may communicate according to a number of wired protocols. Examples of wired protocols may include Universal Serial Bus (USB) communications, RS-232, RS-422, RS-423, RS-485 serial protocols, Firewire, Ethernet, Fibre Channel, MIDI, ATA, Serial ATA, PCI Express, T-1 (and variations), Industry Standard Architecture (ISA) parallel communications, Small Computer System Interface (SCSI) communications, or Peripheral Component Interconnect (PCI) communications, etc.

[0075] Thus, in various aspects, the communications interface 628 may include one or more interfaces, such as, for example, a wireless communications interface, a wired communications interface, a network interface, a transmit interface, a receive interface, a media interface, a system interface, a component interface, a switching interface, a chip interface, a controller, etc. When implemented by a wireless device or within a wireless system, for example, the communications interface may include a radio interface including one or more antennas, transmitters, receivers, transceivers, amplifiers, filters, control logic, etc.

[0076] In various embodiments, communication interface 628 may provide voice and / or data communication capabilities according to a number of wireless protocols. Examples of wireless protocols may include various Wireless Local Area Network (WLAN) protocols, including the Institute of Electrical and Electronics Engineers (IEEE) IEEE 802.xx series, such as IEEE 802.11a / b / g / n, IEEE 802.16, IEEE 802.20, etc. Other examples of wireless protocols may include various Wireless Wide Area Network (WWAN) protocols, such as GSM cellular radiotelephone system protocols with GPRS, CDMA cellular radiotelephone communication systems with 1xRTT, EDGE systems, EV-DO systems, EV-DV systems, HSDPA systems, etc. Further examples of wireless protocols may include wireless personal area network (PAN) protocols such as infrared protocols, protocols from the Bluetooth Special Interest Group (SIG) series of protocols including Bluetooth® specification versions v1.0, v1.1, v1.2, v2.0, v2.0, and one or more Bluetooth profiles, including enhanced data rate (EDR). Other examples of wireless protocols may include short-range communication techniques and protocols such as electromagnetic induction (EMI) techniques. Examples of EMI techniques may include passive or active radio frequency identification (RFID) protocols and devices. Other suitable protocols may include ultra-wideband (UWB), digital office (DO), digital top, trusted platform module (TPM), ZigBee, and the like.

[0077] The system bus 634 interconnects the processor 622, the memory 624, the I / O interface 626, and the communication interface 628, as appropriate. The system bus 634 can be any of several types of bus structures including, but not limited to, a memory bus or memory controller, a peripheral bus or external bus, and / or a local bus using any of a variety of available bus architectures, including, but not limited to, a 9-bit bus, Industry Standard Architecture (ISA), MicroChannel Architecture (MCA), Enhanced ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), PC Memory Card International Association (PCMCIA) bus, Small Computer Peripheral Interface (SCSI), or any other proprietary bus, or any custom bus suitable for a computing device application.

[0078] 7 illustrates a block diagram of an artificial intelligence (AI) system 700 of the autonomous indoor farming system 100 according to some embodiments. In an example embodiment, the AI ​​system 700 includes three layers: a data input layer 702, a data processing layer 704, and a data analysis layer 706. In some embodiments, the AI ​​system 700 in the autonomous indoor farming system 100 is configured to perform at least one of the functions including assessing crop conditions, precisely measuring plant characteristics, optimizing growing conditions to maximize yield at minimum cost, forecasting market demand, and adjusting growing conditions accordingly.

[0079] In some embodiments, the data input layer 702 collects raw data from various sensors of the subsystems to monitor the growth and health of different plants in different trays of different indoor agriculture modules 102 from multiple agriculture facilities 602. The growth and health of the plants can be monitored using image sensors of the vision system 616. In an alternative embodiment, the growth and health of the plants can be monitored by collecting and analyzing soil samples, plant tissue samples, water and air samples. The data input layer 702 can also collect raw data regarding environmental conditions within the growing area, including data from the air circulation system 610, the lighting system 612, the vision system 616, and the liquid circulation system 618.

[0080] The image sensor in the vision system 616 can be used to capture images of the plant in the module 606 during the entire growth process. In some embodiments, the image sensor in the vision system 616 can be used to measure plant characteristics to identify the plant's developmental steps and monitor the response from the plant to the growth environment, such as nutritional deficiencies (i.e., disease detection). In some embodiments, the image sensor can be based on at least one of the following techniques including multispectral and hyperspectral imaging, Raman, thermoluminescence, and photoacoustics. In some embodiments, a combination of these image sensing techniques can provide plant characteristics including leaf temperature, leaf water content, leaf thickness / size, leaf color, chlorophyll content, and photosynthetic efficiency. In some embodiments, at least one image sensor can be used depending on the size of the plant. In some embodiments, multiple image sensors can be used for three-dimensional reconstruction of a large plant or multiple two-dimensional images of the plant to determine optimal growth conditions depending on the plant type. According to various embodiments, various visual characteristics of the plant (e.g., color, size, shape, density, etc.) can be used to determine the health or nutritional deficiencies of the plant. Examples of such plant characteristics that can be detected by the machine vision and running system are described in detail below. In some embodiments, the image sensor in the vision system can be used in conjunction with a light source of the lighting system to provide light intensity and spectrum for plant growth. In some other embodiments, the image sensor of the vision system works with a separate light source of the vision system to perform image collection.

[0081] Image sensors in the vision system 616, as well as other sensors in the lighting system 612, irrigation system 614, air circulation system 610, and liquid circulation system 618, can be used to generate plant characteristic data 814 and environmental data 816, which can include data and information, respectively, regarding the characteristics of the plants and the environment of the agriculture module 606. The plant characteristic data 814 and environmental data 816 can be used to determine plant health, determine actions that may be required to improve plant health, identify plant deficiencies, and / or determine the efficiency and / or productivity of the agriculture module 606.

[0082] As can be appreciated, plants planted or growing in the agriculture module 606 may face health issues over time that may need to be addressed in order to produce healthy plants that produce a desirable amount of crops for farming in the agriculture module 606 and in efficient and profitable alternatives to traditional agriculture. Various sensors in the agriculture module 606 may collect information and store such information as plant characteristic data 814 and / or environmental data 816 to determine one or more deficiencies in the plants growing in the agriculture module 606. In response to determining or identifying whether one or more plants have a deficiency, remedial action may be recommended and communicated to the controller 620 to effect such corrective action in the agriculture module 606.

[0083] One exemplary deficiency that may be identified by the agriculture module 606 is boron deficiency. Boron is a nutrient that maintains the structural integrity of cell walls, transports sugars to the growing point, and functions to cause pollen survival and proper seed formation, as well as nitrogen fixation in legumes. Indicators of boron deficiency may include stunted plant growth, and new leaves or other new growth may appear misshapen or shrunken, as well as distorted outward branching. Another indicator of boron deficiency may include thick, brittle branches and leaves. Yet another indicator may include top growth that shows chlorosis, which appears as yellow spots. In boron-deficient plants, root growth may be restricted, root tips may appear rounded or "rat-tailed," and root hair development may be restricted. Boron deficiency may also be indicated by reduced flowering, cork spot, and cracks on the apple fruit surface. These indicators may appear randomly or irregularly across plants in the agriculture module 606, and plants may be more susceptible to mild injury if they exhibit boron deficiency. These indicators may be captured in the plant characteristic data 814 collected by the agriculture module 606.

[0084] Once a boron deficiency is identified, the agriculture module can implement one or more remedial actions to correct the deficiency. Such remedial actions can include adding boron to the growing medium, soil or hydroponic solution, for example, by adding a foliar boron application. Another remedial action can include balancing the bulk of the nutrient to the plant. Another remedial action can be modifying the pH of the soil or other growing medium. Another remedial action can be addressing boron leaching, which can occur in sandy, porous soils or growing media with low organic matter. Another remedial action can be addressing boron immobilization, which can occur in clayey soils with high iron and aluminum oxide content. Another can be reducing over-irrigation or providing moisture to drought or dry root zones. Another remedial action can be modifying the vapor pressure differential (VPD) that can escape the desired pressure zone or increasing the temperature of a low temperature growing medium. These remedial actions can be implemented, for example, by the controller 620 in the agriculture module 606.

[0085] Another exemplary deficiency that may be identified or determined by the agriculture module 606 is a calcium deficiency. Adequate amounts of calcium are necessary to maintain proper structural integrity of plant cell walls and membranes. Calcium deficiency may cause oxidative stress, triggering appropriate intracellular messaging, and may help regulate nitrogen and sugar flow within the plant. Indicators of calcium deficiency may include distortion of new growth of the plant, such as leaf curl, shepherd's crest symptoms, leaves that turn up or down, etc. Another indicator may include necrosis and / or the appearance of dead edges of the leaves. However, calcium deficiency does not generally affect the mature leaves of the plant. Another indicator may include stunted plant growth and an underdeveloped root system, and the plant may tend to wilt. Another indicator includes interrupted flow and poor fruit set in fruit crops. In tomato plants, calcium deficiency may manifest as end rot. In the fruits, calcium deficiency may manifest as brown necrotic spots (e.g., "cork spots") that may extend to the upper flesh. The sensors of the agriculture module may collect plant characteristic data 814 that may identify such indicators.

[0086] The agriculture module 606 can implement remedial actions that can treat and / or correct the calcium deficiency once such a deficiency is identified. Remedial actions that can be implemented include modifying the pH of the growing medium or irrigation liquid to maintain the pH in a desired range. Correcting the pH can also be accomplished by applying agricultural lime or sulfur to the soil or growing medium. Another remedial action can be to increase the cation exchange capacity (CEC) of the soil or growing medium. The CEC can be increased by increasing the clay and organic matter content in the soil or growing medium. Another remedial action can be to correct the calcium deficiency by adding soluble calcium to the root zone of the plant. Another remedial action can be to apply nutrients at a desired rate. Another remedial action can be to modify the irrigation system to prevent stagnant water from occurring. Another remedial action can be to repair the irrigation system to correct any damage or obstructions or to change the application rate and time. Another remedial action can be to increase the calcium level when nutrient needs increase due to rapid plant growth. This may be especially important when using hydroponics or during plant establishment where high growth is expected to occur. Another corrective action may be to discontinue the use of nutrient sources that may cause excessive changes in pH or limit calcium availability in the root zone. Another corrective action may be to correct problems with environmental control such as temperature issues, vapor pressure differential (VPD), and air circulation. Another corrective action may be to apply a foliar calcium spray. These corrective actions may be implemented by the controller 620 and various systems of the agriculture module 606.

[0087] Another deficiency that can be identified by the agriculture module 606 is potassium deficiency. The agriculture module 606 system can collect plant characteristic data 814 and / or environmental data 816 that can be used to identify potassium deficiency. Potassium is a nutrient that can help maintain cell turgor and open and close the plant's cell stomata. Proper amounts of potassium can also cause proper enzyme activation, leading to indirect regulation of photosynthesis. Indicators of potassium deficiency can include older or lower leaves of the plant showing signs of potassium deficiency. Indicators can include leaf sulfurization or brown necrosis of the leaf edges. In plants with potassium deficiency, the leaf edges may appear dead. Indicators can also include slow plant growth and underdeveloped root systems. Indicators can also include weak stems and leaf stalks, as well as poor fruit quality and yield. Indicators can also include a reduced Brix percentage of the fruit, and the fruit surface may appear rough and dull in color, with uneven discoloration also visible. Additionally, the indicators include indications of plants with water deficiency.

[0088] The agriculture module 606 can implement corrective actions to resolve potassium deficiencies. Corrective actions can include modifying and repairing control systems. Remedial actions can include maintaining adequate oxygenation in the root zone of the plants, and increasing the clay or organic matter content to improve the CEC of the soil or growing medium. Another remedial action can be to apply nutrients in the proper ratio relative to each other, especially with respect to calcium and magnesium. Another remedial action can be to modify the pH to maintain it in a desired range. The temperature of the water applied to the plants can be modified to be within a desired temperature range. Also, nutrient levels can be altered to maintain this level within a predetermined range. Yet another remedial action can be to correct issues with environmental control, such as temperature issues, vapor pressure differential (VPD), and air circulation. These remedial actions can be implemented by the controller 620 and various systems of the agriculture module 606.

[0089] For example, various sensors of the air circulation system 610, the lighting system 612, the irrigation system 614, the vision system 616, and the liquid circulation system 618 can be coupled to the controller 620 and / or the local computer 622. Data acquired from these sensors can be collected and stored in a database. As shown in FIG. 8, computing devices 622 from the multiple agricultural facilities 602 can be coupled to a database 802 that can store data acquired by the various sensors and other information providers shown in the data input layer 702. The computing devices 622 can include one or more engines or modules that can perform the methods described herein. In the example shown in FIG. 8, the computing devices 622 can include a data extraction engine 804, a data processing engine 806, and an agriculture engine 808. In some examples, the data extraction engine 804 can provide the functionality described with respect to the data input layer 702, and the data processing engine 806 can provide the functionality described with respect to the data processing layer 704. Additionally, the agriculture engine 808 can provide the functionality described with respect to the data analysis layer 706. The data extraction engine 804 can be coupled to the database 802 and one or more information sources 812. The data extraction engine 804 can obtain the data needed to determine what, if any, remedial action is required to correct deficiencies that may be identified in plants growing in the agriculture module 606, as well as to train one or more models that may be included in the agriculture engine 808.

[0090] The agriculture engine 808 can include one or more trained machine learning models 820 that are trained using, for example, the plant characteristic data 814. The plant characteristic data 814 can include historical data and information that can be used to characterize various aspects of the plants growing in the agriculture module 606. The plant characteristic data 814 can also include current data and information that can characterize various aspects of the plants. The current data and information can be used by the learning models 820 in the agriculture engine 808 to determine what remedial actions are desired to improve the growth performance of the plants. The current data and information can also be used by the learning models 820 of the agriculture engine 808 to determine whether one or more deficiencies may be present in the plants growing in the agriculture module 606. After such a determination is made by the agriculture engine 808, the information is transmitted to the controller 620 which can then implement such an action by transmitting appropriate agricultural actions 822 to one or more systems of the agriculture module 606, such as the air circulation system 610, the lighting system 612, the irrigation system 614, the vision system 616, and / or the liquid circulation system 618.

[0091] Yet another deficiency that can be identified by the agriculture module 606 is magnesium deficiency. The agriculture module 606 system can collect plant characteristic data 814 and / or environmental data 816 that can be used to identify magnesium deficiencies. A favorable amount of magnesium can cause the proper functioning of many plant enzymes. Magnesium atoms harvest energy from light and also convert photons into energy via electron transport. It can affect the process of photosynthesis in plants because it is a central component in the chlorophyll structure responsible for converting the energy of light into chemical energy.

[0092] Indicators of magnesium deficiency in plants can include pale lower or older leaves compared to their previous color. Leaves of magnesium deficient plants may appear mottled or droopy. Further indicators can include necrotic leaf tissue, which may appear red or purple. Indicators can also include interveinal chlorosis, where there is necrotic tissue between the veins and leaf senescence (or defoliation) may occur.

[0093] The agriculture module 606 can implement remedial actions to address the magnesium deficiency. Such remedial actions can include preventing root zone drying by altering the irrigation period of the plants. In a hydroponic system, the remedial action can include draining liquid from the growing medium between each irrigation period. Yet another remedial action can be altering the pH level of the root zone so that the pH is within a predetermined range. In one example, the range is a pH of about 5.5 or greater. In another example, the pH range is a pH of about 6.5 or less. In another example, the pH range is between about 5.5 and about 6.5. In another example, other ranges can be used.

[0094] Another remedial action may be to maintain the level of magnesium ion concentration at a predetermined level. In one example, the predetermined level of magnesium ion concentration is about 25 ppm. In another example, the remedial action may be to maintain the ratio of magnesium to other nutrients at a predetermined ratio. In one example, the ratio of magnesium to calcium is about 3:1, the ratio of magnesium to potassium is about 4:1, and the ratio of calcium to potassium is about 2:1. Another remedial action may be to maintain PVD at a predetermined level. Another remedial action may be to prevent damage to the xylem of the plant by maintaining light at a predetermined level, maintaining air movement at a predetermined level, and preventing temperature extremes. Another remedial action may be to drain, flush, or refill the nutrient tank to prevent nutrient imbalances. Another remedial action may be to apply a dilute foliar solution of Epsom salts to the plant. Such a foliar solution may include a non-ionic surfactant or a spreader / sticker in the solution. These remedial actions may be implemented by the controller 620 and various systems of the agriculture module 606.

[0095] Yet another deficiency that can be identified by the agriculture module 606 is nitrogen deficiency. The agriculture module 606 system can collect plant characteristic data 814 and / or environmental data 816 that can be used to identify nitrogen deficiencies. Nitrogen is the primary macronutrient that can result in the proper production of proteins, nucleic acids (DNA and RNA), and chlorophyll in plants.

[0096] Plants can exhibit various indicators that allow the agriculture module 606 to identify nitrogen-deficient plants. Indicators of nitrogen deficiency can include a change in the color of the plant. For example, the entire plant can appear pale green. Another indicator can be sulphurization of older leaves of the plant. Another indicator can be new growth points of the plant turning pale green or yellow. Another indicator can be stunted growth of the plant. Another indicator can be leaf drop. Another indicator of nitrogen deficiency can be necrosis of older leaves and lack of necrotic spots. Another indicator can be a decrease in crude protein levels in plant tissues. Another indicator can be reddish color of leaf stems and veins.

[0097] The agriculture module 606 can implement remedial actions to address nitrogen deficiencies. Remedial actions can include adding nitrogen that can be used by the plant roots. Another remedial action can be to correct the pH balance of the soil or growing medium. Another remedial action can be to correct an imbalance between nitrogen and other nutrients. Another remedial action can be to correct an imbalance between nitrogen and other nutrients. A corrective action may be to apply nitrogen fertilizer appropriate for the plant species, growth step, and substrate conditions. Another corrective action may be to apply foliar nutrients to the plants. Another corrective action may be to add additional nitrogen to soils or growing media that have a high amount of high carbon organic matter. Another corrective action may be to inspect and repair monitoring, dosing, and irrigation systems that contain broken, non-functional, or out-of-tolerance components. Another corrective action may be to adjust air circulation and VPD to adjust such conditions to within predetermined ranges. These corrective actions may be implemented by the controller 620 and various systems of the agriculture module 606.

[0098] Yet another deficiency that can be identified by the agriculture module 606 is phosphorus deficiency. The agriculture module 606 system can collect plant characteristic data 814 and / or environmental data 816 that can be used to identify phosphorus deficiencies. Phosphorus is a major macronutrient for plants that is an essential component of nucleic acids, plant reproduction, photosynthesis, and energy transport.

[0099] Plants can exhibit various indicators that allow the Agriculture Module to identify phosphorus deficiency. Indicators of phosphorus deficiency can include purple discoloration of the leaves, especially seen on the undersides of older leaves. Another indicator can be a dark green or purple discoloration of the stems and leaf stalks. Another indicator is discoloration on the leaf margins and tips that gradually progresses inwards. Another indicator is the appearance of leaf tip dieback. Another indicator is necrotic spots that may appear around the leaf margins. Another indicator is leaf drop. Another indicator is reduced flow and fruit. Another indicator is poor root and plant growth.

[0100] The agriculture module 606 may implement remedial actions to address phosphorus deficiencies. Such remedial actions may include applying phosphorus fertilizer in a soluble form. Another remedial action may be applying a phosphorus soil amendment before the crop is established. Another remedial action may be adjusting and / or amending the pH of the soil, growing medium, or nutrient solution. Another remedial action may be applying phosphorus with ammoniacal nitrogen to increase absorption. Another remedial action may be adjusting or increasing the amount of calcium and sulfur to increase phosphorus absorption. Another remedial action may be increasing the temperature of the nutrient solution if the root zone is below a predetermined temperature level. Another remedial action may be applying an organic phosphorus source to the roots of the plants. Another remedial action may be improving root zone aeration. Another remedial action may be foliar fertilization with phosphite. These remedial actions may be implemented by the controller 620 and various systems of the agriculture module 606.

[0101] Yet another deficiency that can be identified by the agriculture module 606 is a sulfur deficiency. The agriculture module 606 system can collect plant characteristic data 814 and / or environmental data 816 that can be used to identify a sulfur deficiency. Sulfur is a secondary macronutrient that can assist in the formation of amino acids, expression of enzymes and vitamins, seed production, chlorophyll formation, oil formation, volatile compound formation, and nitrogen fixation.

[0102] Plants may exhibit various indicators that allow the agriculture module 606 to identify sulfur-deficient plants. Indicators of sulfur deficiency include an overall yellowish green appearance. Another indicator is that yellowing may progress from older leaves to newer leaves. Another indicator is yellowing of the leaf veins. Another indicator is stripping of the leaves. Another indicator is slowed or stunted plant growth. Another indicator is necrotic leaf tips. Another indicator is reduced nitrogen fixation in legumes.

[0103] The agriculture module 606 can implement remedial measures to address sulfur deficiencies. A remedial action may include replenishing the soil, growing medium, irrigation water, or nutrient solution with sulfur within a predetermined range in the form of sulfate. Another remedial action may be to measure and apply nutrients at a predetermined level of sulfur to nitrogen ratio. In one example, the sulfur to nitrogen ratio is about 20:1. Another remedial action may be to monitor, measure, and repair an irrigation system to apply a predetermined level of water volume. Another remedial action may be to increase the amount of organic matter in the soil or growing medium. Another remedial action may be to measure and reduce nitrogen fertilizer applied to the plants. Another remedial action may be to apply a nutrient solution at a temperature within a predetermined temperature range. Another remedial action may be to modify the pH level of the soil, growing medium, or nutrient solution. Another remedial action may be to adjust the VPD in the agriculture module 606. These remedial actions may be implemented by the controller 620 and various systems of the agriculture module 606.

[0104] Referring again to FIG. 7, in some embodiments, the data input layer 702 can capture raw data including market data, production yields, and food quality. Such data can be captured from database 802, or other information servers or other sources. The data collected by the data input layer 702 can be used for spatial and temporal analysis. As mentioned above, examples of plant characteristics that can be detected by the various embodiments are described. For example, if the data collected by the data input layer 702 shows the following plant conditions, such as sluggish plant growth, new leaves / growth appearing pinched or misshapen, distorted outward branching, thick and brittle looking branches and leaves, yellowing, root tips appearing rounded, reduced flowering, cork spots or cracks on the tissue surface, these may be signs of boron deficiency in the plant. When a boron deficiency is detected, the autonomous indoor agriculture system can command the control system to take any of the following remedial actions: correct the pH of the soil, medium, or nutrient solution if it is too high for the particular crop, run self-diagnostics on all environmental control equipment looking specifically for vapor pressure differential (VPD), increase the temperature of the nutrient solution, adjust the irrigation system to increase the amount of water delivered to the roots, apply soluble boron to the soil or medium, or apply a foliar boron spray. The system then continues to monitor the growth and health of the plants and records the effectiveness of the remedial actions.

[0105] As shown in FIGS. 9-13, the vision system 616 of the agriculture module 606 may include an image sensor capable of collecting images of plants that may be growing in the agriculture facility 602. The images may be collected to identify one or more deficiencies that may be present in the plants. For example, as shown in FIG. 9, a leaf 900 of the plant may include edges that exhibit wilting or necrosis 902. Such a condition may be indicative of, for example, a calcium deficiency. In another example, as shown in FIG. 10, an image may show a fruit 1000 that may include one or more spots 1002. For example, uneven discoloration of the fruit 1000 may be indicative of a potassium deficiency. In yet another example, as shown in FIG. 11, a leaf 1100 of the plant may show edges having a first condition at an edge region 1102 and a second condition at an inner region 1104. The color or other characteristics of the edge region 1102 and the inner region 1104 may be indicative of yet another deficiency of the plant.

[0106] Now, referring to FIG. 12, a leaf 1200 can exhibit an edge condition and a spotted condition. In this example, the leaf 1200 includes a discoloration 1202 and an edge with a spot 1204. These characteristics can indicate another deficiency in the plant. In another example, the agriculture module 606 shown in FIG. 13 can operate to determine a deficiency in the plant by monitoring the characteristics of the plant over time. For example, the vision system 616 can capture a first image of a leaf 1300 of a plant at time 1, a second image of the leaf 1300 of the same plant at time 2, a third image of the leaf of the plant at time 3, and a fourth image of the leaf of the plant at time 4. By comparing these images, the agriculture module 606 can detect and / or identify a deficiency in the plant by comparing the plant characteristics over time. As shown, the discoloration 1302 is greater at time 3 than at time 2, and even greater at time 4. Thus, the discoloration is greater over time. It can be seen that the condition increases over time. Additionally, the margins 1304 of the leaves 1300 can be compared over time. As shown, the margins 1304 can be seen to exhibit necrosis. Such a progression can be indicative of a deficiency. In this example, the condition shown can be indicative of a nitrogen deficiency.

[0107] In some embodiments, the vision system 616 can utilize multispectral to detect and collect data reflecting various characteristics of the crop during the growing process. As an example, an RGB camera can be used to collect image data for processing and detecting any abnormal phenomena. In another example, LIDAR can be used to detect and measure plant growth, for example, using leaf area index and spatial heterogeneity information. In another example, thermal imaging can be used to detect irrigation and environmental irregularities (microclimate) around the leaves of the plant. In another example, hyperspectral imaging can be used to detect nutrients, phenotype testing, and measure water content and phytochemical content. The data collected from the vision system 616 can be processed in the processing layer 704.

[0108] In some embodiments, the data processing layer 704 is configured to perform data pre-processing to convert raw data into clean data in an actionable format before providing it to the data analysis layer 706. In some embodiments, the data pre-processing performed in the data processing layer 704 includes data rescaling, data binarization, and data normalization. In some embodiments, the data pre-processing on the images collected by the image sensor of the vision system may include at least one of the steps of resizing the image, removing noise, segmenting, and smoothing.

[0109] In some embodiments, alternative computer vision approaches may be used to improve image filtering, selection, measurement, and analysis. These approaches may include depth mapping, color filtering and extraction, thermal imaging, infrared spectrum differentiation, and feature matching. High resolution images may be cropped into subsections for higher fidelity analysis and accuracy. All intermediate images and associated data may be permanently stored for future modification, tuning, and retraining of artificial intelligence ("AI") models in the data analysis layer 706.

[0110] In some embodiments, the data analysis layer 706 is configured to analyze the data collected in the data input layer 702 based on the above-mentioned plant health indicators that can be used to assess the health and growth condition of the plant. If the system 700 determines that the plant exhibits one of the above-mentioned conditions, the system 700 will instruct the control system to implement remedial action by adjusting certain environmental conditions in the growing area that correspond to the particular detected condition. Thus, the AI ​​system 700 can automatically control the conditions in the growing area, for example, to compensate for any nutrient deficiencies and / or to further optimize the growing conditions for the plants in the plant container modules.

[0111] The AI ​​system 700 can record the collected data and monitor the efficiency of the attempted remedial actions. The data analytics layer 706 compares the growth and health characteristics to evaluate the effectiveness of the remedial actions. Based on the determined effectiveness of the remedial actions, the AI ​​system 700 learns and modifies the algorithm for determining future remedial steps to apply effective remedial actions and not apply ineffective remedial actions.

[0112] In some embodiments, the AI ​​system 700 may include a notification system that notifies authorized / responsible personnel regarding observed / detected crop conditions and applicable remedial actions. Any further remedial actions or plant / crop health or status not accounted for in the system may be reported to the AI ​​system by a human expert connected to the forensic application. Over time, the AI ​​system 700 should approach and achieve greater autonomy where corrective actions are taken by the system to avoid potential errors inherent in human operation. In some embodiments, information about these actions may be logged and communicated to the operator.

[0113] In some embodiments, the data analysis layer 706 is configured to execute machine learning algorithms. In some embodiments, when images are analyzed and classified by a human operator and stored in a database, machine learning datasets will be accumulated in the database that can be used by artificial intelligence (AI) algorithms to automatically analyze and classify future images and make decisions based on such analysis (e.g., that this plant has a particular nutrient deficiency based on the image).

[0114] In some embodiments, the data analysis layer 706 may utilize machine learning models constructed with tree structures of progressive complexity to most accurately assess the subject matter. In another embodiment, the primary machine learning approaches utilized by the data analysis layer 706 may consist of neural networks, deep learning, computer / machine vision techniques, and more traditional machine learning techniques (SVM, KNN, linear regression, logistic regression). Computer vision techniques may include depth mapping, color filtering and extraction, thermal imaging, infrared differentiation, and feature matching.

[0115] In some embodiments, inaccuracies present in the AI ​​model can be corrected by a system-level feedback loop. Existing relevant shaping data can be used to accurately segment ambiguous crop conditions. Inaccurately labeled or predicted images can be fed back through higher layers of the labeling, training, and model building process (data processing layer 704) to continually improve the efficiency of the data analysis layer 706.

[0116] Referring now to FIG. 14, an exemplary method of autonomous farming 1400 is shown. Methods 1400, 1500, 1600 may be performed by one or more of the systems or devices disclosed herein, such as autonomous agricultural facility 100, autonomous agricultural facility 500, or autonomous agricultural facility 600. The steps of the method 1400, 1500, 1600 may be substantially performed by, for example, computing device 622. In other examples, the steps of the method 1400, 1500, 1600 may be performed by a different computing device, such as a server, computer, or other computing device located locally or remotely from farm facility 600. Exemplary computing devices that perform steps of the methods described herein include computing device 622 and remote computer 632. For brevity, method 1400, method 1500 of FIG. 15, and method 1600 of FIG. 16 are described with reference to computing device 622 and agricultural facility 600. However, it should be understood that this step may be performed by other embodiments and variations thereof, as well as by a remotely located shared server, multiple different computing systems, and variations thereof.

[0117] The method 1400 begins at step 1402, where the computing device 622 may acquire plant characteristic data 814. The plant characteristic data 814 may be data characterizing one or more characteristics of a plant growing in a growing container or in the agriculture module 606. The plant characteristic data 814 may be collected by sensors in the air circulation system 610, the lighting system 612, the irrigation system 614, the vision system 616, and the liquid circulation system 618. In one example, the plant characteristic data 814 may include images collected by an image sensor in the vision system 616. The image sensor may collect images of the plants in the agriculture module 606. The plant characteristic data 814 may be collected and stored in the database 802. The computing device 622 may execute a data extraction engine 804 to extract plant characteristic data 814 from the data extraction engine 804. may be used to approach and retrieve plant characteristic data 814 from database 802. The data extraction engine 804 may obtain the plant characteristic data 814 in any suitable manner, such as using a suitable communication method, application protocol interface (API), or the like.

[0118] The method 1400 continues to step 1404 where the computing device 622 can acquire environmental data 816. In one example, the computing device 622 can acquire the environmental data 816 from the database 802 using the data extraction engine 804 in a manner similar to that described above with respect to the plant characteristic data 814. The environmental data 816 can be data characterizing the growing conditions inside the growing module (or growing container) 606. The environmental data 816 can be collected from various sensors in the air circulation system 610, the lighting system 612, the irrigation system 614, the vision system 616, and the liquid circulation system 618 and stored in the database 802.

[0119] At step 1406 of method 1400, the computing device 622 may perform data processing on the plant characteristic data 814 and / or the environmental data 816. The data processing may be performed by the data processing engine 806. The data processing may process the plant characteristic data 814 and / or the environmental data 816 as described above with respect to the functions of the data processing layer 704. The data processing may aggregate, organize, structure, standardize, or perform other data processing to prepare the plant characteristic data 814 and the environmental data 816 for input to the agriculture engine 808. As will be appreciated, the agriculture engine 808 may include one or more machine learning models that may be used to perform other steps of the method 1400.

[0120] In step 1408 of the method 1400, the computing device 622 can determine whether a growth deficiency exists. The computing device 622 can determine the growth deficiency using the plant characteristic data 814 and / or the environmental data 816. In one example, the computing device 622 can include a trained machine learning model 820 for such purpose. The trained machine learning model 820 can be trained using the plant characteristic history data 814 and / or the environmental history data 816. The trained machine learning model 820 can learn indicators of nutrient deficiencies, for example. Each of the nutrient deficiencies described above describes indicators that may be identified by the trained machine learning model. The trained machine learning model can also identify and learn complex relationships between various different data sets in the plant characteristic data 814 and the environmental data 816 to learn indicators of deficiencies that may not otherwise be known. In this respect, the agriculture engine 808 and the trained machine learning model 820 are a significant improvement over traditional agriculture methodologies.

[0121] A growth deficiency can be any deficiency or condition of the plant or growth module 606 that can inhibit healthy and beneficial growth of the plant in the growth module 606. Growth deficiencies can include nutrient deficiencies as discussed above. Additional growth deficiencies can include other deleterious conditions such as over- or under-irrigation of the plant, inadequate air circulation levels, inadequate drainage, inadequate lighting, improper nutrient balance, and the like.

[0122] In step 1410, the computing device 622 determines whether a growth deficiency exists. If the computing device 622 determines that a growth deficiency does not exist, the method returns to step 1402 and the computing device can continue to perform steps 1402 through 1410 continuously or periodically. If the computing device 622 determines that a growth deficiency exists, the method proceeds to step 1412.

[0123] In step 1412, the computing device 622 determines an appropriate The agricultural engine 808 and / or the machine learning model 820 may determine an appropriate agricultural action to be performed in light of the deficiencies determined by the computing device 622. The agricultural action determined by the computing device 622 may be any appropriate response that may be required to correct or remedy the deficiencies determined in step 1408. The agricultural action may be a single action, such as changing the nutrient mix applied to the plant. Also, the agricultural action may be a combination of actions that may include any number of changes to the growing conditions of the growing module 606. The agricultural engine 808 and / or the machine learning model 820 may learn over time the best or appropriate agricultural action to be performed in light of the deficiencies determined by the computing device 622. Thus, the agricultural action may change over time as more data is collected and the machine learning model 820 is retrained, for example, using the method 1600 described below.

[0124] In step 1414, the computing device can transmit an agricultural action 822 to the agricultural controller 620. The agricultural action 822 can include instructions to modify one or more growing conditions in the growing module 606. For example, if the computing device 622 determines that a boron deficiency exists, the computing device 622 can transmit an agricultural action 822 to the agricultural controller 620 instructing the controller 620 to increase an amount of boron in nutrients delivered to the plants in the growing module 606. As a result of such an agricultural action 822, the controller 620 can cause a pump in the liquid circulation system 618 to deliver an increased amount of boron from the nutrient reservoir. As can be appreciated, the agricultural action 822 can include instructions to take any suitable action using one or more of the air circulation system 610, the lighting system 612, the irrigation system 614, the vision system 616, and the liquid circulation system 618.

[0125] In step 1416, the computing device 622 may determine whether the growing process is complete. In some examples, the computing device 622 may determine this based on the amount of time the plant has been grown. In another example, the computing device 622 may use the plant characteristic data 814 to determine whether the growing process is complete. For example, the computing device may determine whether the plant has reached a predetermined size or produced a predetermined amount of fruit. In yet another example, the computing device may continue to perform the steps of the method 1400 until a harvest command is received. If the computing device 622 nevertheless determines that the growing process is not complete, the method 1400 may return to step 1402, and the computing device may perform the method continuously or periodically. If the computing device 622 determines that the growing process is complete, the method 1400 may end.

[0126] As described above, the method 1400 can utilize the trained machine learning model 820 to determine deficiencies that may occur during the plant growth process in the growing module 606. The model 820 can identify plant conditions that may consist of suboptimal yield or suboptimal productivity, after which remedial actions implemented by the agricultural system 600 can improve plant growth. When the agricultural facility 600 is operating, sensors in the air circulation system 610, lighting system 612, irrigation system 614, vision system 616, and liquid circulation system 618 can continue to collect plant characteristic data 814 and environmental data 816. Such data can also be collected after agricultural practices 822 and associated remedial actions have been implemented in the growing module 606.

[0127] 15, there is shown another exemplary method of autonomous farming 1500. Method 1500 includes some aspects substantially similar to the steps and functions described for method 1400. For example, steps 1502 through 1506 may include the same functionality as described in steps 1402 through 1406 of method 1400.

[0128] In step 1508, the computing device 622 The plant growth quality of the plant growing in the growing module 606 can be determined. In one example, the plant growth quality can be determined using the plant characteristic data 814. The plant growth quality can correspond to a deficiency as described above. In another example, the plant growth quality can identify the health of the plant growing in the growing module 606. Any suitable measurement or characteristic can be used to differentiate the plant health and to designate the plant growth quality. In one example, the plant growth quality can be indicated by using one or more individual characteristics of the plant health and / or by using clustering. In such an example, the agriculture engine 808 can use a suitable clustering or binning technique to designate the plant health using categories such as excellent, satisfactory, or deficient plant health. The plant characteristic data 814 can be used to identify whether the plant health is excellent, satisfactory, or deficient in a particular plant, in a particular cart, or in a particular growing module.

[0129] At step 1510, the computing device 622 may determine an agricultural action based on the determined plant growth quality. The agricultural action may be any suitable action as described above with respect to method 1400. For example, the agricultural action may be to change the growing conditions inside the agriculture module 606. In another example, the agricultural action may be to take no action or to continue according to the current growing recipe and the current controlled environment schedule. In yet another example, the agricultural action may be to obtain additional data or information regarding the plant or growing conditions.

[0130] At step 1512, the computing device 622 may determine whether the agricultural practice requires a change in growing conditions in the agriculture module 606. If the computing device 622 determines that a change is required, the method proceeds to step 1514. If the computing device 622 determines that a change in growing conditions is not required, the method returns to step 1502. In this manner, the method 1500 provides continuous or periodic monitoring of plant growth quality of plants in the agriculture module 606.

[0131] At step 1514, the computing device 622 may send agricultural actions to the agricultural controller 620 to implement the changes in growing conditions in the agricultural module 606. As will be appreciated, the agricultural actions may include instructions to perform any suitable actions using one or more of the air circulation system 610, the lighting system 612, the irrigation system 614, the vision system 616, and the liquid circulation system 618, as described above.

[0132] In step 1516, the computing device 622 may determine whether the growing process is complete. In some examples, the computing device 622 may determine this based on the amount of time the plant has been grown. In another example, the computing device 622 may use the plant characteristic data 814 to determine whether the growing process is complete. For example, the computing device may determine whether the plant has reached a predetermined size or produced a predetermined amount of fruit. In yet another example, the computing device may continue to perform the steps of the method 1500 until a harvest command is received. If the computing device 622 nevertheless determines that the growing process is not complete, the method 1500 returns to step 1502, and the computing device may perform the method continuously or periodically. If the computing device 622 determines that the growing process is complete, the method 1500 ends.

[0133] 16, a method 1600 for improving an autonomous agricultural system is shown. The method 1600 begins at step 1602, in which the computing device 622 may acquire plant characteristic history data. The plant characteristic history data may be substantially similar to the plant characteristic data 814 described above. Additionally, the plant characteristic history data may be used by the agricultural system 622 to acquire plant characteristic history data. 00 includes data collected after the training data set used to train the machine learning model 820 that is actively in use at 00. The historical plant characteristic data may be obtained using any suitable method, such as those described above for the plant characteristic data 814.

[0134] In step 1604, the computing device 622 may obtain environmental history data. The environmental history data may be substantially similar to the environmental data 816 described above. The environmental history data may include environmental data collected after the data set used to initially train the machine learning model 820.

[0135] In step 1606, the computing device 622 may retrain the machine learning model 820 using the plant characteristic history data and the environmental history data. Although not shown, the plant characteristic history data and the environmental history data may be processed by the data processing engine 806 as described with respect to the data processing layer 704. The machine learning model 820 may be retrained to take advantage of the increased amount of data available as data continues to be collected by the various sensors of the agriculture module 606. Retraining the machine learning model 820 may result in a retrained machine learning model that may identify different, additional, or modified relationships from the plant characteristic history data and the environmental history data. The machine learning model 820 may be retrained using any suitable open source or proprietary machine learning tool or library.

[0136] In step 1610, the computing device 622 can determine whether the performance of the retrained machine learning model exceeds the performance of the initially trained machine learning model. Any suitable performance matrix can be used to determine the performance of the models. For example, the models can be compared using traditional AB testing methodology. In another example, the retrained model and the initial model can be compared using a common data set of plant characteristic data and environmental data, and then compared to determine whether the models can accurately identify known deficiencies that the agricultural system 600 faced during actual operation. In yet another example, other performance measures can be used.

[0137] If the computing device 622 determines that the retrained model is not performing better than the existing or initial model, the initial model remains operational at step 1616 and the method proceeds to step 1614. If the computing device 622 determines that the retrained model is performing better than the initial model, the method proceeds to step 1612.

[0138] In step 1612, the computing device 622 can replace the initial model with the retrained model. After this replacement, the farming system 600 can continue to operate with the retrained model.

[0139] In step 1614, the computing device may determine whether a predetermined period of time has elapsed. As will be appreciated, an operator of the agricultural system 600 may continually improve the performance of the system by allowing the machine learning model 820 to continually utilize an increased amount of plant characteristic data and environmental data collected during operation of the agricultural system 600. Thus, periodically, the method 1600 may be performed to determine whether newly available data improves the performance of the machine learning model 820. Thus, the computing device 622 may determine whether such a predetermined period of time has elapsed. Any suitable predetermined period of time may be used, such as one week, one month, two months, three months, six months, twelve months, or other periods. Thus, if the predetermined period of time has not elapsed, the computing device 622 waits for the period of time. If the predetermined period of time has elapsed, the method returns to step 1602 to again perform the steps described above to determine whether improvements are made to the operation of the agricultural system 600. Another method may be performed in accordance with the method of FIG. 16, where the computing device 622 determines whether such a period of time has elapsed. In this example, the computing device 622 may wait to perform the method 1600 again until it determines that a sufficient amount of historical plant characteristic data and / or a sufficient amount of historical environmental data allows for retraining of the machine learning model 820.

[0140] The disclosed methods, systems, and apparatus are improvements over conventional methods and systems. Examples described herein include structures and functions not otherwise known. The autonomous agricultural facilities of the present invention allow growers to operate large-scale facilities with little or no human intervention and can include the ability to collect vast amounts of data and information regarding plant characteristics, growth trends, environmental influences on plant growth, as well as the impact of remedial measures when deficiencies are identified and resolved in large-scale agricultural operations. Implementation of machine learning models in the context of autonomous agriculture can improve the consistency, production rates, and profitability of large-scale indoor agricultural operations. Implementation of machine learning models in such a context is significantly improved over known methods due to the unique aspects of the indoor growing environment and its arrangement described herein. Furthermore, the described methods and functions cannot be performed by conventional agricultural methods because the machine learning models can identify relationships between data that cannot be recognized by conventional agricultural methods. Implementation of the methods and systems described herein can result in improved crop yields, improved profitability, and improved production rates compared to conventional methods.

[0141] Although various embodiments of the present invention have been described above, it should be understood that they are presented by way of example only and not by way of limitation. Similarly, the various diagrams provided to enable those skilled in the art to understand the exemplary features and functionality of the present invention may show an example of an exemplary architecture or configuration. However, it will be understood that the present invention is not limited to the exemplary architecture or configuration, and can be implemented using various alternative architectures and configurations. Also, as will be understood by those skilled in the art, one or more features of some embodiments can be combined with one or more features of other embodiments described herein. Thus, the breadth and scope of the present invention should not be limited by any of the exemplary embodiments described above.

[0142] It is understood that any reference to an element herein using a designation such as "first," "second," etc. is not generally intended to limit the quantity or order of those elements. Rather, these designations may be used herein as a convenient means of distinguishing between two or more elements or instances of an element. Thus, a reference to a first and a second element does not imply that only two elements may be used or that the first element must precede the second element in any manner.

[0143] Furthermore, those skilled in the art will appreciate that information and signals may be represented using a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, and symbols that may be referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0144] Those skilled in the art will further appreciate that the various example logic blocks, modules, processors, means, circuits, methods, and functions described in connection with the aspects disclosed herein may be implemented by electronic hardware (e.g., digital implementations, which may be designed using source coding or other techniques, analog implementations, or a combination of the two), various forms of program or design code incorporating instructions (which may be referred to herein for convenience as "software" or "software modules"), or a combination of both. To clearly illustrate the interchangeability of such hardware and software, the various example components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. It will be understood that the various example components, blocks, modules, circuits, and steps may be implemented in connection with the aspects disclosed herein, whether such functionality is implemented as hardware, firmware, software, or a combination of such techniques. Whether or not to use the described functionality is determined by the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

[0145] Further, those skilled in the art will appreciate that the various example logic blocks, modules, devices, components, and circuits described herein may be implemented in or executed by integrated circuits (ICs), which may include general purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), other programmable logic devices, or any combination thereof. The logic blocks, modules, and circuits may further include antennas and / or transceivers for communicating with various components within the network or device. The general purpose processor may be a microprocessor, but alternatively, the processor may be a conventional processor, controller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in combination with a DSP core, or any other suitable configuration for performing the functions described herein.

[0146] If implemented in software, the functions may be stored as one or more instructions or codes in a computer-readable medium. Thus, the steps of a method or algorithm disclosed herein may be implemented as software stored in a computer-readable medium. Computer-readable media includes both computer storage media and communication media, including any medium that can transfer a computer program or code from one place to another. A storage medium may be any available medium that can be accessed by a computer. By way of example, such computer-readable media may include, but is not limited to, RAM, ROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic disk storage devices that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer.

[0147] As used herein, the term "engine" refers to software, firmware, hardware, and any combination of these elements for performing the associated functions described herein. Moreover, for purposes of discussion, the various engines are described as separate engines. However, one of ordinary skill in the art will appreciate that two or more engines may be combined to form a single engine that performs the associated functions in accordance with embodiments of the present invention.

[0148] Additionally, memory or other storage devices and communication components may be utilized in embodiments of the invention. It will be appreciated that for clarity, the above description has described embodiments of the invention with reference to different functional units and processors. However, it will be apparent that any suitable distribution of functionality between different functional units, processing logic elements, or domains may be used without departing from the invention. For example, functionality illustrated as being performed by separate processing logic elements or controllers may be performed by the same processing logic element or controller. Thus, references to specific functional units do not indicate a strict logical or physical structure or organization, but merely to suitable means for providing the described functionality.

[0149] The term "model" as used in this disclosure includes data models generated using deep learning. Deep learning is a type of machine learning that can include training models in a supervised or unsupervised setting. Deep learning models can be trained to learn relationships between various data sets. Deep learning models can be trained to learn relationships between various data sets. Deep learning models may be based on a set of algorithms designed to model abstractions in data using multiple processing layers. The processing layers may be composed of nonlinear transformations. Deep learning models may include, for example, neural networks, convolutional neural networks, and deep neural networks. Such neural networks may be composed of levels of trainable filters, transformations, projections, hashing, and pooling. Deep learning models may be used for large-scale relationship recognition tasks. Models may be generated using a variety of open source and proprietary machine learning tools known to those skilled in the art.

[0150] Various modifications to the implementations described in the present invention will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other implementations without departing from the scope of the present invention. Thus, the present invention is not intended to be limited to the implementations shown in this specification, but is to be accorded the widest scope consistent with the novel features and principles disclosed herein, as recited in the appended claims.

Claims

1. acquiring plant characteristic data characterizing one or more plant characteristics of a plant growing in at least one growing module; determining at least one growth deficiency of the plant based on the plant characteristic data using an agricultural engine; and 1. An autonomous agricultural system comprising: a computing device configured to transmit at least one agricultural action to an agricultural controller operably coupled to at least one growing module, the at least one agricultural action including a remedial action for improving growing conditions of a plant.

2. The autonomous farming system of claim 1 , wherein the agriculture engine comprises a trained machine learning model.

3. The autonomous agricultural system of claim 1 , wherein the plant characteristic data is collected by one or more sensors located on the at least one growing module.

4. 2. The autonomous agricultural system of claim 1, wherein the computing device is configured to acquire plant environment data characterizing growing conditions of the plant within the at least one growing module, and the at least one growth deficiency is determined based on the plant environment data.

5. The autonomous farming system of claim 4 , wherein the agriculture engine includes a machine learning model trained using historical plant characteristic data and historical environmental data.

6. The autonomous agricultural system of claim 1 , wherein the agricultural controller is operably coupled to at least one of an air circulation system, a lighting system, an irrigation system, a vision system, and a liquid circulation system.

7. 7. The autonomous system of claim 6, wherein the at least one agricultural action includes instructions to at least one of the air circulation system, the lighting system, the irrigation system, the vision system, and the liquid circulation system to change growing conditions in a growth module.

8. The autonomous agricultural system of claim 1 , wherein the plant characteristic data comprises image data collected by a vision system in the growing module, the image data comprising images of plants in the growing module.

9. The autonomous farming system of claim 8 , wherein the agriculture engine automatically determines at least one deficiency of a plant based on image history data.

10. 2. The autonomous agriculture system of claim 1, wherein the computing device is configured to retrain a machine learning model of the agriculture engine and to replace an initial machine learning model with the retrained machine learning model when the computing device determines that performance of the retrained machine learning model surpasses performance of an initial machine learning model.

11. the at least one growing module includes a plurality of growing containers; the plurality of growing containers are arranged into at least two columns and at least two rows to define a matrix of growing containers; 2. The method of claim 1, wherein the computing device is configured to obtain plant characteristic data characterizing one or more plant characteristics of a plant growing in each of the plurality of growing containers. Autonomous agricultural system.

12. acquiring plant characteristic data characterizing one or more plant characteristics of a plant growing in at least one growing module; determining at least one growth deficiency of a plant based on the plant characteristic data using an agriculture engine; and transmitting at least one agricultural action to an agricultural controller operably coupled to the at least one growing module, the at least one agricultural action including a remedial action for improving growing conditions for plants.

13. The method of claim 12 , wherein the agriculture engine comprises a trained machine learning model.

14. The method of claim 12 , wherein the plant characteristic data is collected by one or more sensors located on the at least one growing module.

15. 13. The method of claim 12, further comprising acquiring plant environment data characterizing growing conditions of a plant within the at least one growing module, and determining at least one deficiency further based on the plant environment data.

16. The method of claim 12 , wherein the agriculture engine includes a machine learning model trained using historical plant characteristic data and historical environmental data.

17. The method of claim 12 , wherein the agricultural controller is operably coupled to at least one of an air circulation system, a lighting system, an irrigation system, a vision system, and a liquid circulation system.

18. 20. The method of claim 17, wherein the at least one agricultural action includes instructions to cause at least one of the air circulation system, the lighting system, the irrigation system, the vision system, and the liquid circulation system to change growing conditions in the growth module.

19. The method of claim 12 , wherein the plant characteristic data comprises image data collected by a vision system in the growing module, the image data comprising an image of the plant in the growing module.

20. 20. The method of claim 19, wherein the agriculture engine automatically determines at least one deficiency of the plant based on image history data.

21. 13. The method of claim 12, further comprising: retraining an initial machine learning model of the agricultural engine; and replacing the initial machine learning model with the retrained machine learning model if the computing device determines that performance of the retrained machine learning model surpasses performance of the initial machine learning model.

22. the at least one growing module includes a plurality of growing containers; the plurality of growing containers are arranged into at least two columns and at least two rows to define a matrix of growing containers; The method of claim 12 , further comprising obtaining plant characteristic data characterizing one or more plant characteristics of a plant growing in each of the plurality of growing containers.

23. A plurality of enclosed growth containers, each containing an independently controllable environment, a plurality of enclosed growth containers oriented into at least two columns and at least two rows to define a matrix of tenas; at least one movable cart positioned in each of the plurality of enclosed growing containers, each configured to support one or more plants; 1. An autonomous agricultural device comprising: an articulated robot movably positioned on a track and a riser, the articulated robot configured to move in a first direction along the track to access each row of the growing container matrix and to move in a second direction along the riser to access each row of the growing container matrix.

24. 24. The autonomous agricultural device of claim 23, wherein an articulated robot is configured to extract and insert the at least one movable cart from each growing container in the matrix of growing containers.

25. 24. The autonomous agricultural device of claim 23, wherein each growing container in the matrix of growing containers includes at least two stages configured to support the at least one movable cart at different positions relative to a bottom of the growing container.

26. 24. The autonomous agricultural apparatus of claim 23, further comprising a computing device and an agriculture controller, the computing device coupled to the agriculture controller and configured to determine at least one deficiency of one or more plants using the trained machine learning model.

27. 27. The autonomous agricultural device of claim 26, wherein the computing device determines the at least one deficiency based on plant characteristic data obtained from one or more sensors located in each of the growing containers.

28. 30. The autonomous agricultural device of claim 27, wherein the computing device determines the at least one deficiency further based on environmental data obtained from one or more sensors located in each of the growing containers.

29. 30. The autonomous agricultural device of claim 28, wherein a machine learning model is trained using historical plant characteristic data and historical environmental data obtained from sensors located in each of the growing containers.

30. 30. The autonomous agricultural apparatus of claim 29, wherein the computing device is further configured to transmit agricultural actions to one of an air circulation system, a lighting system, an irrigation system, a vision system, and a liquid circulation system located in one of the plurality of growing containers, such that one of the air circulation system, the lighting system, the irrigation system, the vision system, and the liquid circulation system alters growing conditions in one of the plurality of growing containers.

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