Smart autonomous vertical farming system

Abstract

A smart Autonomous Vertical Farming System in an indoor farm comprising a Computer server, a Planning and Control system, a Library containing images and reference data, a plarlity of wireless routers for data transmission to and from all parts of the indoor farm. with routes for movement inside and through the Vertical Farming System. The indoor farm is provided with a plurality of racks with a plurality of racks with a plurality of trays of plants placed on the racks, wherein new data are gathered by a plurality of Autonomous Mobile Robots (AMR), said data including images of the plants, health condition and growth status of the plants obtained from the AMRs. The data transmitted through the wireless routers to the Computer Server, wherein the Computer Server schedules daily operations for the AMRS and maintenance works to be carried out by the AMRs autonomously transitted to each AMR. The 3D positioning tags, wireless beacons and designated landmarks in the indoor farm form a Route Guidance System, for movements of the AMRs for the daily operations and maintenance within the vertical farming system and the movements are planned and executed using the Route Guidance System.

Description

[0001] TITLE OF THE INVENTION

[0002] SMART AUTONOMOUS VERTICAL FARMING SYSTEM

[0003] FIELD OF INVENTION

[0004] The present invention relates to the operations of a Smart Autonomous Vertical Farming System.

[0005] BACKGROUND

[0006] Tremendous improvements have been made in the field of indoor vertical farming. As indoor vertical farming is carried out in urban and city areas, improvements in productivity is to offset the higher costs of resources in urban and city areas.

[0007] Some indoor vertical farms have adopted semi-automated systems, integrating sensors for monitoring environmental conditions. However, these systems may still lack real-time adaptability and precision. Full automation, incorporating Al, Unmanned Aerial Vehicles (UAVs), and autonomous mobile robotics (AMRs), represents the next frontier. Despite the potential advantages, drawbacks include high initial setup costs, technological complexity, and the need for skilled personnel to manage and maintain these systems.

[0008] Challenges faced in the industry include the limited scalability of current automation solutions, difficulty in adapting to diverse crop varieties, and potential resistance to embracing new technologies. Additionally, power consumption and energy costs associated with maintaining optimal environmental conditions pose ongoing challenges. Balancing the upfront investment in automation with long-term operational benefits remains a critical consideration for many indoor farms. A major costs component is manpower costs. While traditional farming uses unskilled labour, vertical farms require skilled technicians (with higher costs) to maintain complex operations of the vertical farm. As the industry navigates these challenges, the development and adoption of more sophisticated, cost-effective, and scalable automation solutions will be pivotal for the future of indoor vertical farming. DISCUSSION OF PRIOR ART

[0009] US Publication No 2019 / 0307077 A1 discloses a vertical farming system including a storage structure having racks of storage shelves for housing plant-carrying containers. Mobile robots travel around the racks to transfer containers of plants to and from the storage shelves. Under direction of a central control system, one or more mobile robots may transport a container from a storage location to a workstation. Once there, care may be provided for the plant, including water and / or other nutrients, and data may be gathered on the plant. This may be done by an owner of the plant, or by an automated service robot positioned at the workstation. Data gathered on the plant, including for example photographs, may be sent by email or other com-munications schemes to an owner of the plant. The use of automated service robots is not fully autonomous.

[0010] US 2023 / 0255153 A1 discloses an improved vertical farming system , including improved farming shelves and racks, efficient transport of grow trays in a cluster system, novel plant-grow trays or pallets, which allow for combined optimal plant growth and irrigation methods, novel harvesting methods, novel modular lighting, novel light intensity management systems, real time vision analysis that allows for the dynamic adjustment and optimization of the plant growing environment, novel camera mobility systems, and a novel rack structure system that allows for simplified building and enlarging of vertical farming rack systems. The system uses autonomous systems and methods for growing edible plants, using improved scalable stacking and shelving units configured to allow for scaling or increasing the size of the system through additional shelving units. Finally the vertical farming system photographs and records the plant life cycle by 1) high definition cameras located on telescoping poles using one or more gimbals to allow the cameras to record each plant, the telescoping system capable of transport between shelves; 2) a camera vehicle attached to a rail or rail system above the plant-grow trays, capable of independent movement to record each plant; and / or 3) autonomous flying drones, incorporating three dimensional / multispectral cameras, flying preprogrammed routes, thereby reducing or eliminating certain labor costs.. The result is to record images and / or video of the growing plants, automatically analyze these images and videos in real-time, thereby understanding exactly what the plant needs for optimal growth, while retaining the database for future plant growth. Although the use of Drone is mentioned in the Abstract, its workings in this invention was not described except illustrated in Fig 7D. There was also no claim involving the use of drone. US Publication 2023 / 0309477 discloses a vertical micro-farm system wherein an autonomous robot is configured to move baby plants from the nursery to the plurality of grow towers after completion of the first growth process; and a pick and place robot for moving plants within the system, performing tasks including processing and packing the produce cultivated in the microfarm.

[0011] The subject invention further improves autonomous systems for indoors vertical farming by relying on real-time location 3-D coordinates system for autonomous navigation especially indoors and with centimeter-level accuracy. Drones cannot rely on GPS indoors as these are unable to receive signals indoors. Further improvements may be made to such indoors vertical farming system, even to enhance use of autonomous flying drones.

[0012] PROBLEM TO BE SOLVED

[0013] While traditional farming uses unskilled labour, vertical farms require unskilled labour (for manual work) as well as skilled technicians to maintain operations of the vertical farms. Since Vertical farms are largely located in urban areas and in the cities, land costs and labor costs have become a large component of operational costs of Vertical Farms. As land and labor costs increase in city areas, operators of indoor vertical Farming has to rely on more automated systems for improved productivity and lower operational costs, by using all ora combination of most of these productivity solutions :-

[0014] 1. Precision Agriculture: Advanced technologies, such as Artificial Intelligence (“Al”), Unmanned Aerial Vehicles (“UAVs”), and Autonomous Mobile Robots (“AMRs”), enable precision agriculture, optimizing resource use and minimizing waste.

[0015] 2. Automation and Robotics: Implementing autonomous systems like UAVs and AMRs eliminates manual labor dependencies, improves efficiency and reduces operational costs.

[0016] 3. Data-Driven Management of Vertical Farms making it fully autonomous: Real-time data collection and analytics provide comprehensive insights, allowing for proactive decision-making and addressing issues before they impact crop health. 4. Resource Optimization: Integration of Al enables intelligent control of environmental factors, optimizing resource consumption and promoting sustainable farming practices.

[0017] 5. Improved Crop Monitoring: UAVs equipped with sensors and AMRs facilitate constant monitoring, ensuring uniform crop growth and quality throughout the vertical farm.

[0018] In current Vertical Farming systems, horticulture LED Lighting are used in lieu of sunlight to grow produce and edible plants and vegetables. The success rate of using visual navigation aids for autonomous navigation of UAVs is low when LED lighting is used. This is because LED Lighting will cause interference to visual detection of ArUco marker tags and hence, cause disruption in its motion planning.

[0019] Use of Drones or UAVs relying on Global Positioning System (GPS) 3D coordinates is also almost impossible indoors due to weak reception of signals from satellites.

[0020] Next, using the Drone’s in-built Flight Controller’s barometric sensor to determine the height of the UAV is very inaccurate at times due to some indoor environmental conditions. Hence, an external 3D Positional Coordinates Tags is proposed for the use of UAVs in autonomous indoors Vertical Farming System.

[0021] Hence, the terms “Autonomous” and “indoor” were used to be mutually exclusive. The subject invention is to make it possible for farming indoors to be autonomous.

[0022] There are patents using Robotic operating system (ROS) software to do SLAM (Simulated Localization and Area Mapping) indoor for drones. However, Linux OS in Computer Systems to operate drones is quite time consuming to set this up. Also, the precision accuracy is not well guaranteed for this case.

[0023] It would be preferable if all daily operations could be planned and managed by drones and robotic machines, resulting in an autonomous Smart Vertical Farm. Since such an autonomous Smart Vertical Farm would be monitored continuously using drones and robots, such autonomous Smart Vertical Farms would be managed and controlled autonomously and continuously through the gathering of data on each plant in each part of the Vertical Farm, analysis of data obtained from each plant, assessing their condition and if there is any deficiency or non-conformance to data records on growth of such plants, the Autonomous Smart Vertical Farming system would initiate plans to remedy such situations, direct the drones and robots to attend to such deficiencies and after taking action to continuously monitor the condition of these plants. Such a system would therefore not depend on manual labour and skilled technicians to manage operations in the Vertical Farm and could be independently managed by the autonomous system.

[0024] In arriving at the Autonomous Smart Vertical Farming System, several inventive applications have been created to solve the problems arising from creating such a system.

[0025] A new approach to guide movements of drones and robots in an enclosed environment has been proposed. The subject invention proposes using 3D Positional Tags and Landmarks for use indoors to guide drones in vertical farming. The same method would also be used to guide and move Automated Mobile Robots (AMRs) to move around within the Smart Vertical Farm, carrying out maintenance tasks which previously have been performed manually.

[0026] SMART AUTONOMOUS OPERATIONS AND ARTIFICIAL INTELLIGENCE

[0027] Daily and routine operations of a Vertical Farm could be managed on a Smart Autonomous scale thus overcoming issues faced by current Vertical Farms:-

[0028] 1. Limited Efficiency: Conventional indoor farming often relies on manual and skilled labor and lacks precision due to human error, leading to inefficiencies in resource utilization and crop management.

[0029] 2. Uniformity Issues: Achieving consistent crop quality and yield across the entire vertical farm is a common challenge due to variations in environmental conditions.

[0030] 3. Manual Labor Dependency: Labor-intensive processes, such as planting, monitoring, and harvesting, are not only resource-intensive but also prone to human error. For Vertical Farms which are highly automated, there is the need for trained technicians in addition to manual labor.

[0031] 4. Data Fragmentation: The absence of real-time data collection and centralized monitoring results in limited insights into the overall health and status of the crops. 5. Human omission: Due to the height of the racks on which trays of plants are grown in, human error occurs when the plants on the higher racks are not easily checked and problems with such plants are often missed out.

[0032] PROBLEM TO BE SOLVED

[0033] An integrated approach to use of automation and Al and Big Data to autonomously operate a Smart Autonomous Vertical Farm has to be implemented in order to reduce reliance on skilled technicians to operate and maintain the Vertical Farms and reduce use of unskilled laborers for routine check and maintenance of the plants.

[0034] The invention focuses on the seamless integration of Al, Autonomous Unmanned Aerial Vehicles (UAVs) for data gathering and Autonomous Mobile Robots (AMRs) for executing routine tray placement and extractions, such that all daily operations and routine management of the Vertical Farming System are carried out autonomously.

[0035] The inventors propose the triangulation algorithm of using 4 beacons to get the 3D Positional Coordinates (x,y,z) in centimeter level accuracy and Dijkstra’s shortest path algorithm together for motion planning which requires only Windows OS and PCs, which saves on resources. Once drones can operate autonomously indoors in 3D Space, it should be relatively simpler for AMRs to use the same Positional Coordinates to operate autonomously indoor in 2D Space to perform functions in attending to the plants with precision guidance. The inventors have observed that from an engineering perspective, it is crucial to make the algorithm as simple as possible.

[0036] These technologies not only operate autonomously but also enable all components of the Vertical Farming System to communicate intelligently with each other as well as relaying data collected to the main server, where such data are analyzed and maintained for reference. Imagine an indoor vertical farm where UAVs, equipped with advanced sensors, survey the Smart Autonomous Vertical Farming system, collecting real-time data for analysis by the servers. The analysis would include comparison of images of plants taken by UAVs during routine inspection flights with historical data of the same plants as well as comparison of images of plants with standard images of diseased plants, maintained in the computer systems. Based on the data analysis, the computer servers would then plan corrective action and maintenance tasks, for the next day. The corrective action and maintenance tasks would be assigned to specific AMRs and UAVs. Their route to each tray in each rack would be programmed into the AMR and UAV.

[0037] The AMRs and UAVs, each uploaded with instructions for specific tasks for the day such as corrective action, maintenance instructions and routes would then proceed to fulfil these relayed instructions. The UAVs would then navigate the Vertical Farm efficiently, using the 3D Positional Coordinates and landmarks laid in the Vertical Farm to execute tasks like precision harvesting, pollination of flowers and monitoring. After the UAVs have completed their tasks, AMRs are then sent out to attend to routine tasks such as rotation of trays from one location to another. The UAVs are then send to monitor and obtain images of the plants and area to confirm all tasks have been correctly completed and the plants are in good condition. This synergistic collaboration between AMRs and UAVs, driven by artificial intelligence, not only maximizes efficiency but revolutionizes the way we cultivate crops indoors, ushering in a new era of smart and sustainable agriculture.

[0038] The Smart Autonomous Vertical Farming environment is a closed system. Use of Artificial Intelligence by deployment of UAVs in a closed system faces some disadvantages:-

[0039] • Reliance on Global Positioning System (GPS) 3D coordinates is also almost impossible indoors due to weak signals to capture satellites indoor.

[0040] • Use of the in-built Flight Controller’s barometric sensor of a drone to determine the height of the UAV is very inaccurate at times due to some indoor environmental conditions.

[0041] • Robotic operating system (ROS) software to do SLAM (Simulated Localization and Area Mapping) is not practical as the precision accuracy required for maneuvering is not sufficient.

[0042] For a closed system such as the Vertical Farming system, the Inventors propose use of an inventive external 3D Positional coordinates Tag working together with use of landmarks (ArUco tags or other fiducial marker system) to overcome these disadvantages. The entire layout of the Vertical Farming System may be mapped out with detailed landmarks (ArUco tags or other fiducial markers) and routes. Deployment of Low Frequency Beacons (Autonomous navigation capability for UAV) and shortest path algorithms would enable the computer system to plan each route to be taken by the UAV and allow the UAV to fly above each rack and obtain images of the plant at each precise location which would be stored for future action. The same Autonomous Navigation system would be used by AMRs which would move between aisles of racks.

[0043] Useful features of the innovative solution:

[0044] • Usage of AMRs to perform tasks like inserting and extracting trays from each rack.

[0045] • Low Frequency Beacons (Autonomous navigation capability for UAV and AMR).

[0046] • Crop Monitoring using Al Algorithms to detect crop diseases, monitor crop growth and estimate harvesting date based on leaf surface area.

[0047] • Data collection such as usage of lightweight Volatile Organic Compound (VOC) sensors to detect specific levels of Terpenes, Formaldehyde, Benzene, Toluene, Xylene, Ethylene, Methane, Acetone, Ethanol, and Isopropanol and detecting harmful range.

[0048] SUMMARY OF INVENTION

[0049] A first object of the Invention is a Smart Autonomous Vertical Farming System in an indoor farm comprising a Computer Server, a Planning and Control System, a Library containing images and reference data and a plurality of wireless routers for data transmission to and from all parts of the indoor farm, characterized in that the indoor farm is provided with a plurality of 3D positioning tags and designated landmarks to form a plurality of routes for movement inside and through the Vertical Farming System, and the indoor farm is provided with a plurality of racks which are spaced apart forming an aisle between each rack, and each rack is also spaced apart vertically from the rack above and the rack below, with a plurality of trays of plants placed on the racks, said plants growing with indoor lightings and an irrigation system employed to nourish the said plants with water and nutrients, wherein new data are gathered by a plurality of Autonomous Mobile Robots (AMR), said data including images of the plants, health condition and growth status of the plants obtained from the AMRs, said data transmitted through the wireless routers to the Computer Server, wherein the Computer Server schedules daily operations for the AMRs and maintenance works to be carried out by the AMRs autonomously. wherein said daily operations and maintenance works to plant, grow, tend and harvest the plants are autonomously transmitted to each AMR through the wireless routers by the Computer Server, wherein the 3D positioning tags and designated landmarks in the indoor farm form a Route Guidance System, for movements of the AMRs for said daily operations and maintenance within the Vertical Farming System, and said movements are planned and executed using the Route Guidance System.

[0050] Preferably, the images of the plants are transmitted to the Computer server and are compared with those in a database on growth and health conditions of the plants as well as historical data of the plants kept in the Computer Server, to determine the best mix of nutrients and the indoor lightings for optimum growth of the plants.

[0051] Preferably, the instructions for the daily operations and maintenance are worked out by the Computer Server and are transmitted to each AMR, said instructions including tasks to be performed, the route within the indoor farm to be taken by each AMR to carry out the daily operations and maintenance works.

[0052] Preferably, the plurality of 3D positioning tags and landmarks in the indoor farm form the basis of a Route Guidance System, and data obtained daily from movements of the AMRs are updated to the Route Guidance System.

[0053] Preferably the route and the instructions to the AMR include sensing the 3D Positional Tags and matching the images of designated landmarks with the instructions issued to each AMRs so as to navigate from one location to another within the indoor farm.

[0054] Preferably, the AMRs include:-

[0055] An Unmanned Aerial Vehicle (UAV), modified to fly indoors;

[0056] An Autonomous Mobile Robot Lifter (“AMR Lifter”);

[0057] An Autonomous Mobile Robot Transporter (“AMR Transporter); and

[0058] A Tiered Trolley placed on the AMR Transporter. Preferably, the AMR Transporter and the AMR Lifter having a charger and the AMR Lifter and the AMR Transporter are connectable to a mobile charger when both are not in use.

[0059] Preferably, each AMR is equipped with a Real-time triangulation locating system comprising a plurality of cameras and sensors and a wireless data transmitter to enable the AMR Lifter to move in the indoor farm, using the sensors and the cameras to locate the 3D positional tags and landmarks in the indoor farm, sending the data including the position and the location thereof to the Computer Server, and receiving further instructions and data to enable the AMR Lifter to execute tasks along the aisles and to locate the position of trays and other objects for performance of task assigned.

[0060] Preferably the UAV is adapted to fly indoors by using the 3D Positional Tags, Low Frequency Beacons and designated landmarks to fly from one location to another location within the indoor farm, and wherein the 3D Positional Tags, Low Frequency Beacons and designated landmarks are used for route guidance and navigation, on the basis of the daily maintenance work planned by the Computer Serverand changes in each flight route updated to the Route Guidance System.

[0061] Preferably the UAVs are equipped with an extensible probe with a plurality of sensors including VOC sensors to detect air conditions including specific levels of Terpenes, Formaldehyde, Benzene, Toluene, Xylene, Ethylene, Methane, Acetone, Ethanol, and Isopropanol ranging around the plants in the racks.

[0062] Preferably each AMR Lifter is equipped with a plurality of cameras, sensors and wireless data transmitter to enable movement of AMR Lifter in the indoor farm, using the sensors and the cameras to locate 3D positional tags and landmarks in the indoor farm, and wherein the wireless data transmitter sends data including the position and the location thereof to the Computer Server, and receives further instructions and the data to enable movement of AMR Lifter along the aisles and to locate the position for performance of task assigned.

[0063] Preferably the AMR Lifter is provided with a flexible extensible arm with a gripper device at one end thereof to grip and to remove the trays. Preferably, the camera at the end of the flexible extensible arm of the AMR Lifter is used to guide the gripper device to grip and also to release its grip in order to move the trays from one position to another.

[0064] Preferably the AMR Lifter use the camera with the Real Time Locating System by the AMR Lifter to guide the flexible extensible arm with the gripper device to grip and place or remove the trays on the racks.

[0065] Preferably, the AMR Lifter is provided with a camera at the end of the flexible extensible arm thereof to enable the AMR Lifter to use its triangulation system to triangulate its position relative to the landmarks and computes the necessary adjustments to ensure precise alignment, to commence gripping or release of trays.

[0066] Preferably, the AMR Lifter removes the trays with the plants therein from one rack and places the trays onto the AMR Tiered Trolley, which would transport these trays and placed these trays onto another rack in another location, as part of the growing program of these plants.

[0067] Preferably, the AMR Lifter and the AMR Tiered Trolley works togetherto remove and / orto replace the trays from one location to another location within the vertical farming system.

[0068] A secondary object of the Smart Autonomous Vertical Farming System is a Tiered Trolley is provided with a network of water pipes that run along each layer or the shelf of the Tiered Trolley to a reservoir at the base thereof for collecting wastewater spilled from the trays so that the spilled wastewater is collected in the reservoir.

[0069] A third object of the Smart Autonomous Vertical Farming System is the Route Guidance System which has a plurality of 3D Positional Tags and designated landmarks in the indoors farm works together to form a route network for movement of the UAVs and AMRs, and details or changes in said route network obtained from previous flights or movements of the UAVs and AMRs are updated to the Route Guidance System in the Computer Server.

[0070] Preferably the Computer Server works out a route for each AMR Lifter and AMR Transporter to take whenever the AMR Lifter and AMR Transporter are instructed to carry out a series of tasks, and such routes are constantly updated when the AMR Lifter and AMR Transporter transmits data of the surrounding thereof.

[0071] Preferably the Computer Server works out a route for the UAV to undertake whenever the UAV is assigned to carry out a series of tasks, and the route is constantly updated when the UAV transmits data of the surrounding thereof.

[0072] Preferably the data obtained from the sensors and / or cameras by the UAVs are transmitted to the Computer Server for analysis of the condition of health and status of the plants by matching the new data against the stored historical data of the plants and checking against the library of data on health and other conditions of these plants.

[0073] Preferably, the landmarks used in the Smart Autonomous Vertical Farming System include wireless beacons and fiducial markers placed in the indoors farm.

[0074] BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Fig. 1 is an overview of the Vertical Farming system of this invention.

[0076] Fig. 2 is a perspective view from the front of the main components of the Vertical Farming system of this invention.

[0077] Fig. 3 is another perspective view (from one side) of the main components of the Vertical Farming system of this invention. Fig. 4A is a perspective view of the UAV (Drone) proposed for use in this invention. Fig 4B is a view of the UAV from its back.

[0078] Fig. 5 is a perspective view of the UAV moving through the aisles in between the vertical farming system.

[0079] Fig 6A and Fig 6B shows how the Real-Time Locating System in an UAV of the invention works when it reaches a point in the route for image capturing.

[0080] Fig 7A is an illustration of how an UAV approaches structures like Racks containing trays of plants in a Vertical Farm. Fig 7B is an example of how the UAV triangulate when it reaches a point in the route for image capturing.

[0081] Fig. 8A is a perspective view of the Autonomous Mobile Robot (AMR) of this invention, which consists of a Lifter AMR and Tiered Trolley and a Transport AMR. Fig 8 B is a perspective view of a Lifter AMR. Fig 8C is a perspective view of the Transport AMR with the Tiered Trolley for carrying trays (with plants).

[0082] Fig. 9A is a perspective view showing the Lifter AMR and Transport AMR moving through the aisles in between the racks of the Vertical Farming System. An UAV is also shown to complete the use of Autonomous Mobile Robots in daily and routine tasks such as inspection and monitoring of the plants by UAVs and maintenance of plants by Lifter AMR and TransportAMR in the Smart Autonomous Vertical Farming System.

[0083] Fig 9B shows the interaction between the Lifter AMR and Tiered Trolley with the trays in the racking system. Fig 9C is a perspective view of the Tiered Trolley which is transported by the Transport Robot.

[0084] Fig. 10A, 10B are perspective views of the Lifter AMR carrying out a tray removal operation, firstly identifying the specific tray, then the lifter arm extending to remove a tray from the rack.

[0085] Fig 10C is a close up view of the gripper of the Lifter AMR. Fig 10D shows the Lifting Arm of the Lifter AMR returning from the Tiered Trolley after placement of a tray containing plants (removed from the rack). After the placing of the specified number of trays onto a Tiered Trolley, these trays would to be transported by the Transport AMR, for placement in another position in the racking system.

[0086] Fig. 11 is a perspective view of the Transport AMR with a Tiered Trolley after the Tiered Trolley had received a tray from the Lifter AMR and the Transport AMR with the Tiered Trolley on it is about to move off after completion of a transfer of Trays from the Racking System to the Tiered Trolley.

[0087] Fig 12 is a diagram of how the UAV navigates a part of a Strawberry Vertical Farming System showing the take off point.

[0088] Fig 13 is an illustration of the types of images captured by the UAV as it flies along its programmed route, obtaining images of the plants placed on the racks in various sections of the Strawberry Vertical Farming System.

[0089] Fig. 14 is a diagram of how the UAV navigates a part of a Strawberry Vertical Farming System showing the UAV returning to its landing point.

[0090] DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

[0091] Fig. 1 is an overview of the Smart Autonomous Vertical Farming system of this invention.

[0092] Vertical Farming systems are implemented in urban and city areas, where land costs is high. Prior vertical farming systems have been implemented successfully and proved to be commercially successful. However vertical farming is not only affected by rising land costs, its operations are also affected by high labor costs and high operational costs including water and electricity and raw material costs. In order to maintain profitability, vertical farming operations are increasing turning to automation to keep costs pressures in place. Many operations within the Vertical Farming System have been introduced to make Vertical Farming feasible such as water and nutrient supply and dosing systems, water re-cycling systems, lighting systems, tray systems for keeping plants at various stages of growth and tray moving systems. The vertical farm indoor facility consists of multiple levels or racks equipped with grow lights, irrigation systems, and climate control mechanisms to support the growth of crops in a controlled environment. Each level contains trays or containers filled with growing medium and plants, arranged in a dense and efficient layout to maximize space utilization. These features are known in Vertical Farming System and are used to increase productivity in Vertical Farms.

[0093] Due to the height of the plants growing in trays placed on multi tiered racks, human inspection of the health and other conditions of plants placed at higher levels of the racks often pose difficulty to human inspection. Due to lighting conditions, and the number of plants involved, human inspection of plant condition are often not thorough leading to omission of problems (such as plant disease) until a sufficiently large number of plants are involved. By then, the costs and time taken to attend to such problems would be high.

[0094] The use of Smart Drones (UAVs) and Autonomous Mobile Robots (“AMR”), all equipped with cameras and other types of sensors are proposed to overcome the difficulties encountered in effectively monitoring the health and other conditions of plants. However the use of Artificial Intelligence (“Al”) in conjunction with UAVs and AMRs for improved efficiency faced these problems:-

[0095] • Horticulture LED Lighting used in Vertical Farming Systems in lieu of sunlight hinders use of visual navigation aids for autonomous navigation of UAVs.

[0096] • Use of UAVs relying on Global Positioning System (GPS) 3D coordinates is also almost impossible indoors due to weak reception of signals from satellites.

[0097] Solutions for precise locations include an inventive Route Guidance System with

[0098] • Use of an external 3D Positional Coordinates Tags to enable navigation of not only UAVs but AMRs indoors is proposed for precise navigation and movement in the Vertical Farming System.

[0099] • Additional Use of Landmarks (ArUco tags and other fiducial markers) to further improve accuracy in navigation of UAVs and AMRs. • Use of wireless routers to transmit data and images collected by the UAVs to the computer servers and to enable transmission of data (instructions and routes) from the computer servers to the UAVs and AMRs.

[0100] The inventors have now turned their attention to using Autonomous Mobile Robots (“AMRs”) and drones or Unmanned Aerial Vehicles (“UAVs”) in the Vertical Farming System. UAVs would gather data on the growth of produce in the vertical farming system, to plan and implement operational daily work schedules in the Vertical Farming System such as checking on condition of the produce, spotting for signs of disease or sickness in the produce and attending to such conditions as early as possible. AMRs consisting of AMR Lifter (40) working with AMR Transport (50) and Tiered Trolley (60) would carry out mundane but necessary work like moving trays of produce for maintenance, rotation of trays of produce from one growth stage to the next growth stage, for increased productivity. These tasks are managed by a Planning and Control System (12) in the Vertical Farm.

[0101] The data gathered by the UAVs are collated by various sensors and cameras in the UAVs and analyzed by the Library and Database (14) in the Computer systems (11) which control the operations of the Vertical Farming system (10). The images captured are analyzed by the computers and any possible deviation (in terms of color, condition of the leaves and so on) are matched against standard images of plants in various stages of growth in the Library and Database (14) in the computer database. If deviations are spotted, the computer would use the data obtained to plan the additional inspection for the plants in that location to confirm whether there is any problem with the plants. If the plants are not healthy, the AMR Lifter (40) would remove the tray (with the plants) for further maintenance. The computers would continue to plan and program daily work and operations including daily routes for each AMR and each UAV (20) for the next cycle of growth of the plants. .

[0102] The entire Vertical Farm system is connected to a Computer Server (11), using wireless communication (17) to transfer data from each drone, each AMR (20, 40,50) and other data collection points (62) in the Vertical Farm System. In this manner, the Vertical Farming System would use UAVs, equipped with cameras and sensors to survey each plant in the vertical farm, collecting real-time data. This real-time data is then collated by the Computer servers, and analyzed on a real-time basis, matching past records of images of the same plants as well as comparing these images with library of standard images for each type of plant and each stage of growth . Based on the daily inputs provided from each UAV, the work plans for the next day would be prepared by the Computer system. Based on the work plans, instructions with planned routes are then relayed to UAVs. The UAVs would then follow the route plan and continue to gather data as well as carry out assigned tasks, including precision harvesting, pollination of flowers and monitoring of the health of plants. After the completion of the tasks, data including images of the plants attended to would be relayed back to the computer system. Fresh instructions would be issued to the AMRs to carry out follow up operations, going to each specified tray location to carry out maintenance work such as transfer of trays. This synergistic collaboration, driven by artificial intelligence, not only maximizes efficiency but further reduces manpower required to carry out daily operations in a Smart Autonomous Vertical Farming System.

[0103] Fig. 2 is an front view of the main components of the Smart Autonomous Vertical Farming system of this invention.

[0104] Fig. 3 is another perspective view (from one side) of the main components of the Smart Autonomous Vertical Farming system of this invention.

[0105] Referring to Fig 2 and Fig 3, the vertical farm indoor facility consists of multiple levels or racks (18) equipped with grow lights, irrigation systems, and climate control mechanisms to support the growth of crops in a controlled environment. Positional wireless low frequency beacons (71 ,72) and 3D Positional tags (“landmarks” and “ Fiducial Markers” ) are placed at many locations with the Vertical Farming System, including the racking system as well as each rack level.

[0106] Each level of racks (15) contains trays or containers filled with growing medium and plants, arranged in a dense and efficient layout to maximize space utilization. An aisle (19) would separate one row of racks from another row of racks. All these are common to Vertical Farms and would not be described in detail herein. A problem arising from the number of racks especially those racks at the higher levels, is the lack of access for inspection of plants placed there. UAVs with cameras and other types of sensors would be ideal for the work. However UAVs which rely on external GPS signals cannot work well in a Vertical Farm. The height sensors of UAVs also do not work precisely in an indoor environment.

[0107] Positional Coordinate Tags (71) are placed along strategic locations together with “landmarks” (Low Frequency Beacons 72) to enable routes for the AMRs and UAVs to be planned to enable these to navigate from one location to another. Low Frequency Beacons (72) are also positioned on top of the racking system (18) and other strategic locations in the Vertical Farm to guide UAVs. While the AMR which consists of a Lifter Robot and a Transport Robot moves along the floor of the Vertical Farm, the UAVs would fly between the aisles of the racks (15) at designated height above the ground and over each level of rack, in accordance to its flight plan, as programmed by the Computer Server.

[0108] Landmarks

[0109] Here’s a more detailed description of landmarks in the context of Smart Vertical Farming System of this invention:-

[0110] 1. Distinctive Features: Landmarks can be various types of objects, structures, or patterns that stand out from their surroundings. These features may include corners, edges, doors, windows, signage, equipment, or any other visual or physical elements such as Fiducial Markers that are unique or easily distinguishable within the environment. For the UAVs of this invention, these are guided by fiducial markers and Low Frequency Beacons and other landmarks. AMRs Lifter and AMR Transport use both landmarks and Fiducial markers to enable these to move around as well as perform lifting, placing and other operations.

[0111] 2. Recognition and Detection: AMRs use sensors such as cameras, LiDAR (Light Detection and Ranging), or laser scanners to detect and recognize landmarks within their surroundings. These sensors capture data about the environment and analyse it to identify specific features that match predefined criteria for landmarks.

[0112] 3. Localization Reference: Once identified, landmarks serve as reference points for the robot to determine its own position and orientation within the environment. By triangulating its position relative to multiple known landmarks, the robot can accurately localize itself and maintain awareness of its whereabouts as it navigates through the environment. 4. Dynamic Adaptation: Landmarks can be static or dynamic, depending on the environment and the application. Static landmarks remain fixed in position, providing reliable reference points for navigation and localization. Dynamic landmarks may change over time due to factors such as moving objects, changes in lighting conditions, or alterations to the environment, requiring the UAVs and AMRs to adapt its navigation and mapping strategies accordingly.

[0113] Overall, landmarks are essential elements in the navigation system of AMRs (using the Ruote Guidance System) and Real Time Locating Systems of AMRs, enabling them to effectively perceive, interpret, and interact with their environment to perform a wide range of tasks autonomously and efficiently.

[0114] Fig. 4A is a perspective view of the UAV (Drone) proposed for use in this invention. Fig 4B is a view of the UAV from its back.

[0115] The UAV typically cannot operate in an indoor environment, facing two problems:-

[0116] (a) Global Positioning System (GPS) 3D coordinates are almost impossible indoors due to weak reception of signals from satellites.

[0117] (b) A Drone’s in-built Flight Controller’s barometric sensor to determine the height of the UAV in an indoor environment is very inaccurate at times due to some indoor environmental conditions.

[0118] Hence, the inventors propose the use of an external 3D Positional Coordinates Tags and Low Frequency Wireless Beacons and landmarks to plan flight routes for UAVs in Smart Autonomous indoors Vertical Farming System.

[0119] Referring to Fig 4 and Fig 4A, the UAV(20) is equipped to operate in tight and confined spaces when flying through each level of the racking system. The UAV has a camera (21) at its front as well its rear (24). It has 3D Positioning Low Frequency Beacon (72) to communicate with the Low Frequency Beacon transmitters (72) placed in various locations in the Indoors Farm and in particular various locations in the Racking System (18). The same navigational features being Landmarks (70), Low Frequency Beacons (72) and 3D positional Tags (71) are used for planning of routes of UAVs which would fly systematically in between racks (15) , taking images of each product and transmitting images and other data to the Computer Server for further analysis.

[0120] To enable the UAV (20) to perform its tasks, the UAV is equipped with a camera (21) at its front and its back (24). It has an extendable probe (23), the end of which is a VOC sensor (22). Since the UAV (20) flies through the space above the trays of plants and beneath the above (overhead) rack, it has also a backward sensing camera (24) for collision avoidance. Due to the tight spaces the UAV has to fly through, 3D positional tags (71) and Low Frequency Beacons (72) are positioned in the racking system to enable the UAV to navigate through the tight spaces. The same landmarks (70), 3D positional tags (71) and Low Frequency Beacons (72) are also used by the Route Guidance System (13) in the Computer Server to plan the route for each UAV to take, in order to carry out its assigned work daily. As the UAV files through the same route, its cameras would record slight changes, if any, in position of plants and the Computer Server then update the route so that each time a UAV flies through a certain route, the latest available route guidance is provided to the UAV to enable it to carry out its assignment without any mishap.

[0121] UAV are also equipped other types of sensors such as lightweight Volatile Organic Compound (VOC) sensors (22) to detect specific levels of Terpenes, Formaldehyde, Benzene, Toluene, Xylene, Ethylene, Methane, Acetone, Ethanol, and Isopropanol and detecting harmful range.

[0122] The UAVs are used for:-

[0123] ■ Crop Monitoring using cameras and Al Algorithms to detect crop diseases, monitor crop growth and estimate harvesting date based on leaf surface area.

[0124] ■ Data collection on health condition, detection of plant diseases and detection of growth stage using lightweight Volatile Organic Compound (VOC) sensors to detect presence of specific levels of Terpenes, Formaldehyde, Benzene, Toluene, Xylene, Ethylene, Methane, Acetone, Ethanol, and Isopropanol and communicating such data back to the Computer System to match against records of known levels indicating whether the plants are in good healthy condition and whether the plants have any type of disease. The UAVs are equipped with an extensible probe (23) to allow it to take air sample in front of its flight path to detect the presence of VOC. The UAVs would go to each level of the rack flying through the rack, above the plants systematically, taking images of the plants as it flies over them, sending data to the computer server.

[0125] Fig. 5 is a perspective view of the UAV moving through the aisles in between the vertical farming system. A description of how the UAV works in the Vertical Faming System is given herein.

[0126] Fig 6A and Fig 6B shows how the real-Time Locating System in an UAV of the invention works when it reaches a point in the route for image capturing.

[0127] The entire layout of the Vertical Farming System is mapped out with fiducial markers and Low Frequency Beacons (72) which serve as landmarks for routes. Deployment of Low Frequency Beacons (72) for autonomous navigation capability by UAVs would enable a detailed layout of the Vertical Farm to be mapped out. The 3D Positional Tags, Low Frequency Beacons and Fiducial markers thus function as “ landmarks” in the Vertical Farming System enabling a detailed map for movements of an UAV from point to point to be built. These routes would form the basis of a Route Guidance System (13) in the computer system (11). The Computer System (11) would plan each route to be taken by the UAV. When an UAV fly over a designated location, it would obtain images of the plants at each precise location which would be stored for future action. Should there be changes, such changes would be updated in the route guidance system to ensure all movements are based on the latest available route updates.

[0128] Use of UAVs in Vertical Farming operations

[0129] The vertical farm is organized according to the age of the produce, having sections for seedlings, sections for young plants, section for pollination of plants and sections for plants ready for harvesting. These plants are placed in racks and the plants are placed on trays which are put on the racks. The UAV (20) would be assigned routes and tasks based on the work program planned by the computer system, based on data obtained the previous day and in response to the analysis of data obtained on the health and other conditions of the plants. The UAV (20) would fly along the aisle, then over each layer of the racking system, between the upper and the rack it is flying, guided by a real-time location 3-D coordinate system for autonomous indoors navigation. Data route instructions transmitted to the UAVs enable it to navigate along various heights of the aisles, between sections of the vertical farm, based on programmed navigation routes, using in-built Flight Controller height sensors and the 3D positional Coordination Tags (71) and Low Frequency Beacons (72) to determine the flight pattern and height of the UAV.

[0130] Checking for Health Condition

[0131] As the UAV move along sections of the Vertical Farm in its flight program for inspection of health of the plants, the on board cameras of the UAV would record the coIor and condition of the plants. The data would be transmitted to the computer servers and images matched against images of health plants and diseased plants stored in the library of plants. If some images obtained by the UAV indicate a plant (at a designated location within the vertical farm) is sick, the data would be recorded and collated for an AMR to carry out inspection and removal of the plant, later on.

[0132] The UAVs have sensors to collect lightweight Volatile Organic Compound (VOC) to detect specific levels of Terpenes, Formaldehyde, Benzene, Toluene, Xylene, Ethylene, Methane, Acetone, Ethanol, and Isopropanol. The VOC data are transmitted back to the computer servers and checked whether harmful levels of these volatile compounds are detected. If the VOC sensors detect these are in harmful range, the operators of the Vertical Farming System would be alerted and corrective action implemented.

[0133] As the UAV move along sections of the Vertical Farm in its flight program for inspection of readiness of the plants for harvesting, the on board cameras of the UAV would record the color and condition of the plants. The data recorded would be transmitted to the computer servers (11) and images matched against images of plants stored in its library of images (14) of plants ready for harvesting. If the images obtained by the UAV indicate a plant (at a designated location within the vertical farm) is ready for harvesting, the data would be recorded and collated for an AMR to carry out the harvesting operation later on. of conditions of each type of condition

[0134] The UAV would carry out daily inspections of each section of the Vertical Farm in its flight program to inspect the general health condition of the plants. The on board cameras of the UAV would record the color and condition of the plants. The data recorded would be transmitted to the computer servers and images matched against historical records of plants (maintained in the database (14) of the Computer Server) , to assess whether the plants are generally healthy. The use of UAVs would not only be faster but more accurate than a manual inspection but also enable all levels of the plants in the vertical farming system to be inspected properly. The health and other operational parameters of the plants would be stored to enable immediate action if some plants were diseased.

[0135] Use of UAV in pollination

[0136] The UAV takes on the role of pollinator, assisting in flower pollination while also performing ripe detection and monitoring VOC (Volatile Organic Compound) gases. Equipped with specialized tools and sensors, the UAV autonomously navigates through the rack, scanning for ripe flowers with its advanced imaging system. Using Al algorithms, the UAV identifies flowers at the optimal stage for pollination, ensuring efficient fertilization and fruit development.

[0137] Simultaneously, the UAV's VOC sensors continuously monitor the levels of volatile organic compounds emitted by the plants. These sensors detect subtle changes in the chemical composition of the air surrounding the plants, providing valuable insights into plant health and stress levels. By analyzing VOC data in real-time, the UAV can identify potential issues such as disease outbreaks, nutrient deficiencies, or environmental stressors, enabling timely intervention to mitigate risks and optimize crop health.

[0138] As the UAV identifies ripe flowers for pollination, it carefully maneuvers to each flower, depositing pollen with precision to facilitate fertilization. This autonomous pollination process helps ensure consistent fruit set and yield for the crop, enhancing overall productivity and quality. By combining pollination services with ripe detection and VOC sensing capabilities, the UAV offers a comprehensive solution for indoors vertical farming, providing growers with valuable data and assistance to optimize crop management practices and maximize harvest yields. This innovative approach underscores the potential of advanced technology to revolutionize agricultural processes and address challenges in food production sustainability.

[0139] Fig 7A is an illustration of how an UAV approaches Racks containing trays of plants in a Vertical Farm.

[0140] Since use of GPS indoors is unsuitable, and the in-built barometric sensors of a drone could be very inaccurate when used indoors, the entire Vertical Farming has 3D Positional Coordinates Tags (71) laid on many locations in the racks and Low Frequency Beacons (72) on top of each rack to serve as landmarks in the Vertical Farm. The entire layout of the Vertical Farm is thus mapped out with these detailed landmarks to form a plurality of flight routes, which are programmed into the Route Guidance System (13) of the Computer System (11). Changes to detailed landmarks and routes are updated to the computer system to ensure that the routes for UAVs are accurate and correct. Finally, Triangulation of Low Frequency transmissions (Autonomous navigation capability for UAV) would enable the computer system to plan each route to be taken by the UAV and allow the UAV to obtain images of the plant at each precise location which would be stored for future action.

[0141] Fig 7B is an example of how the UAV triangulate when it reaches a point in the route for image capturing, using a 3D triangulation algorithm as shown:-.

[0142] 30 Trianguiation Bfgorithm using 4 anchors & 1 tag All these daily tasks - be it in health monitoring or daily inspection or pollination - are enabled through use of the Real-Time Locating System with Real time triangulation algorithms in the UAVs working with the Route Guidance System (13).

[0143] Use of Autonomous Mobile Robots (AMR) in Vertical Farming

[0144] Fig. 8A is a perspective view of the Autonomous Mobile robot (AMR) of this invention, which consists of a Lifter AMR and Tiered Trolley and a Transport AMR.

[0145] Fig 8B is a perspective view of a AMR Lifter (30).

[0146] Fig 80 is a perspective view of the AMR Transport (50) with the Tiered Trolley (60) for carrying trays (with plants).

[0147] There are two types of AMR - a Lifter AMR (or AMR Lifter) and a Transport AMR (or AMR Transport). The terms “ AMR Lifter" and “Lifter AMR” are used interchangeably just as for" AMR Transport “ and " Transport AMR”. Both types of AMR move around the Vertical Farming System and along the aisles between the racks of the Vertical Farming System. When not in use, the Lifter AMR and the AMR Transport would be connected to a Charging Base which would recharge both the AMR Transport and AMR Lifter.

[0148] A description of the AMR Lifter is given with reference to Fig. 8B. A drawing of the Robotic Arm (extended) is also shown. The AMR Lifter has these features:-

[0149] 1. Base Joint (31): The base joint (31) is the point where the robotic arm connects to the main body of the AMR Lifter. It allows the arm to rotate horizontally, usually providing the first degree of freedom (DOF) for the arm's movement.

[0150] 2. Shoulder Joint (32): The shoulder joint (32) is typically the joint closest to the base joint. It allows the arm to move up and down or in a lifting motion. This joint provides the second DOF for the arm's movement.

[0151] 3. Elbow Joint (33): The elbow joint (33) is located between the shoulder and wrist joints. It enables the arm to bend or extend, providing the third DOF for the arm's movement. 4. Wrist Joint (34): The wrist joint (34) is located at the end of the robotic arm, closest to the gripper. It allows the arm to rotate horizontally or vertically, providing additional flexibility in positioning the gripper. This joint often provides the fourth and fifth DOFs for the arm's movement.

[0152] 5. Gripper (35): The gripper (35) is the end-effector of the robotic arm, responsible for grasping, holding, and releasing objects. It may come in various designs, such as claws, suction cups, or specialized tooling, depending on the application requirements.

[0153] 6. Lower Links (36): Lower links are the segments of the robotic arm between the base joint, shoulder joint, elbowjoint, and wrist joint. They provide structural support and facilitate the movement of the arm by transferring motion from one joint to another.

[0154] 7. Mast (37): This term usually refers to a feature of the AMR that allows its height to be modified or adapted to different requirements or environments. It could involve mechanisms such as telescopic columns, hydraulic or pneumatic lifts, or adjustable legs. The purpose of height adjustability is often to accommodate varying payload sizes, navigate different terrain types, or interact with equipment or objects at different heights.

[0155] 8. Lifter Mechanism (38): The lifter mechanism is a component of the AMR that enables it to lift or lower objects, pallets, or other materials. This mechanism may include hydraulic or pneumatic actuators, scissor lifts, or other mechanisms designed to provide vertical movement. The lifter mechanism allows the AMR to interact with objects at various heights, facilitating tasks such as loading and unloading goods onto shelves or conveyor systems.

[0156] 9. Tracking Sensor with Camera (39): This component typically consists of a sensor system integrated with a camera that enables the AMR to track its position and surroundings. The sensor may use various technologies such as LiDAR (Light Detection and Ranging), radar, or depth cameras to detect obstacles, map the environment, and navigate autonomously. The camera provides visual information that aids in object recognition, navigation, and decision-making for the robot. These features allow the AMR to carry its tasks in real time, transmitting data back to the Planning and Control System, having the images scanned with images stored in the Reference Library as well as past images of the same plant. Fig. 8C is a perspective of the Transport AMR with the Tiered Trolley for carrying Trays. The Tiered Trolleys have these new and inventive features:-

[0157] Maximizing Storage Capacity: Trolleys with multiple layers provide ample storage space for trays, allowing for the efficient transportation of a large number of crops in a single trip. By stacking trays vertically on the trolley's multiple layers, the available floor space can be utilized more effectively, maximizing the amount of produce that can be transported at once.

[0158] Organization and Sorting: The multiple layers of the trolley allow for organized sorting and categorization of trays based on factors such as crop type, growth stage, or destination within the facility. This helps streamline the workflow by ensuring that trays are easily accessible and can be efficiently retrieved or delivered as needed.

[0159] Facilitating Automated Handling: AMRs can autonomously navigate around the facility and interact with the trolleys to load or unload trays. The multiple layers of the Tiered Trolley provide flexibility for the AMR Lifter to access and manipulate trays efficiently, allowing for seamless integration of automated handling processes into vertical farming operations.

[0160] Optimizing Resource Utilization: By using Tiered Trolleys with multiple layers, vertical farming facilities can optimize resource utilization by reducing the number of trips required to transport trays between different areas. This improves overall operational efficiency and helps minimize the time and energy spent on manual handling tasks.

[0161] Fig. 9A is a perspective view showing the AMR Lifter and AMR Transport moving through the aisles in between the racks of the Vertical Farming System. An UAV is also shown to complete the use of Autonomous Mobile Robots in daily and routine tasks of a Smart Autonomous Vertical Farm e g. inspection and monitoring of the plants by UAVs and maintenance of plants and moving of plants within the Vertical Farm by Lifter AMR and Transport AMR.

[0162] Fig. 9B shows the interaction between the Lifter AMR and Tiered Trolley with the trays in the racking system.

[0163] Fig. 9C is a perspective view of the Tiered Trolley which is transported by the Transport Robot.

[0164] An illustration of how the Lifter AMR and Transport AMR Robot works together in daily maintenance operation is described with reference to Fig. 9A, Fig. 9B and Fig. 9C. How AMR Lifter move from one rack to another rack in another location

[0165] Fig. 9B shows the start of the process of retrieving a tray positioned in the middle of two sides on a growth rack.

[0166] The camera and sensors of the Real Time Locating System in the AMR Lifter (30) plays a crucial role in ensuring accurate and efficient operation. As the Lifter AMR (30) approaches the designated retrieval location, its camera sensor (39) scans the environment for landmarks (71) such as Fiducial Markers positioned on both sides of the rack. These landmarks serve as reference points that guide the Lifter AMR (30) to align itself precisely with the target tray. The camera (39) captures images of the landmarks (71) from two different angles, allowing the Lifter AMR Real Time Locating System to determine its position relative to the retrieval location. Using this information, the Lifter AMR (30) adjusts its trajectory and orientation to approach the tray placement area accurately. Once in position, the camera sensor (39) may perform a final scan to confirm the alignment and ensure that the retrieval manoeuvre can be executed smoothly. Any discrepancies or obstacles detected during this process are swiftly identified and accounted for, allowing the gripper (35) of the AMR Lifter to retrieve the tray (16) with precision and reliability. This systematic approach .enabled by the camera sensor’s capabilities, facilitates seamless retrieval operations in environments such as vertical farms or indoor agricultural facilities, where precise manipulation of trays is essential for efficient harvesting and maintenance tasks.

[0167] Trays are always supplied with water from the irrigation system in the racks. When trays are placed on the racks, the plants in the trays are nourished through an irrigation system which supplies both water and nutrients to the plants.

[0168] Referring to Fig. 9B, when the Lifter AMR (30) picks a tray of produce, the tray (16) would have some water on it. When a tray (16) is picked up and placed on the Tiered Trolley (60), the water in that tray would likely be spilled. Also as the Tiered Trolley moves, being transported by the Transport AMR, water again is likely to be spilled onto the Tiered Trolley. The Tiered Trolley is therefore designed with a network of water pipes to take spilled water from trays on each layer to a reservoir at its base. The inventive features of the Tiered Trolley are described in reference to Fig. 9C.

[0169] 1. Trolley Design: The Tiered Trolley is equipped with a network of water pipes that run along each layer or shelf of the trolley. These pipes are strategically placed to collect wastewater from trays or containers holding plants or growing medium.

[0170] 2. Tray Drainage: Each tray or container on the trolley has drainage holes or channels designed to allow excess water to flow out. As plants are watered or irrigated, any excess water or nutrient solution drains from the trays into the pipes on the Tiered Trolley.

[0171] 3. Gravity Flow: The water pipes on each layer of the Tiered Trolley are angled or sloped towards a central collection point or drain at the base of the Tiered Trolley. This design allows gravity to facilitate the flow of wastewater from the upper layers down towards the reservoir at the bottom.

[0172] 4. Reservoir at the Base (61): At the base of the Tiered Trolley, there is a reservoir or collection tank that stores the wastewater drained from the trays. This reservoir may have a capacity suitable for holding large volumes of water to accommodate the drainage from multiple trays over an extended period.

[0173] 5. Reuse or Disposal: The wastewater collected in the reservoir can be reused for irrigation or nutrient replenishment in the vertical farming system, depending on its quality and nutrient content. Alternatively, if the wastewater is considered waste or unsuitable for reuse, it can be disposed of appropriately, following local regulations and environmental guidelines.

[0174] By incorporating water pipes on the Tiered Trolley to drain wastewater from trays to a reservoir at the base to be re-used, the system helps maintain optimal moisture levels for plant growth while preventing waterlogging or oversaturation. It also facilitates efficient water management and resource conservation in vertical farming or hydroponic environments.

[0175] Fig. 10A, 10B are perspective views of the Lifter AMR carrying out a tray removal operation, firstly identifying the specific tray on a rack, then extending the lifter arm and operating the gripper to remove a tray from a rack.

[0176] Fig. 10C is a close up view of the gripper of the Lifter AMR. Fig. 10D shows the Lifting Arm of the Lifter Robot returning from the Tiered Trolley after placement of a tray containing plants into the Tiered Trolley (having been removed from the rack). After the placing of the specified number of trays onto a Tiered Trolley, these trays would to be transported by the Transport AMR, for placement in another position in the racking system.

[0177] The workings of the Lifter AMR and Transport AMR are described with reference to Figs 10A, 10B, 10C and 10D.

[0178] Daily movements of the Transport AMR (50) and Lifter AMR (30) are programmed by the Computer System. The work program for each Transport AMR (50) and Lifter AMR (30) is planned based on the reports and images on the health and other conditions of the plants. The route for each Transport AMR (50) and Lifter AMR (30) is then programmed into each AMR. Each Transport AMR (50) and Lifter AMR (30) may work independently of each other or may work together, based on the work plans for each AMR, which again are planned based on analysis of earlier data transmission of condition of the plants, collated by the UAVs during its flight routines. Based on the data collated and analyzed, a work plan including route is prepared and transmitted to each Lifter AMR and Transport AMR.

[0179] Based on the work plan assigned, a Lifter AMR may be sent to a specific location in between aisles to pick up a tray of produce, which had been detected to be in an unhealthy condition. A Transport AMR would follow behind. The Transport AMR would have a Tray Trolley placed on it.

[0180] Referring to Fig. 10A, the Lifter AMR has an extensible arm at the end of which is a gripper. The extensible arm also has one or more cameras (39) to guide the gripper (35) in its operations. On being guided to the precise location by the landmarks (Fiducial Markers) to where the plant is placed, the Lifter AMR would extend its arm to the height of the rack where the tray with the plant is located. The gripper (35) would be guided to the exact location of the plant and tray. A camera (39) in the gripper (35) would capture the landmarks (Fiducial markers). The Lifter AMR would then proceed to use its gripper to grip the tray in which the plant is placed. The tray and the plant is then brought down to be placed on the Tiered Trolley.

[0181] Precise identification

[0182] Referring to Fig. 10B, the Lifter AMR (30) relies on its camera and sensors of its Real-Time Locating System to navigate towards the designated destination for identification of the tray to be removed. The Lifter AMR (30) relies on its camera sensor (39) to scan the environment for landmarks positioned on both sides of the intended placement area. These landmarks are Fiducial Markers (71) which serve as reference points that guide the Lifter AMR in aligning the tray accurately between them. As the Gripper of the Lifter AMR approaches the target, its camera captures images of the landmarks from two different perspectives. By analysing these visual cues, the AMR's Real Time Locating System triangulates its position relative to the Fiducial Markers (71) and computes the necessary adjustments to ensure precise alignment. Once positioned correctly, both sides of the tray is securely gripped, and the camera sensor (39) perform a final scan to verify the accuracy of the placement. Any discrepancies are detected and corrected in real-time, ensuring that the tray is positioned exactly where intended. This process facilitates efficient and reliable tray placement, crucial for tasks such as material handling, inventory management, and logistics within indoor environments like the Smart Autonomous vertical farms.

[0183] Referring to Fig. 10C, when the Lifter AMR has identified the tray to be taken out of the racking system, the AMR’s Real Time Locating System triangulates its position relative to the landmarks and computes the necessary adjustments to ensure precise alignment. Once positioned correctly, the tray (16) is securely gripped between the two sides, and the camera sensor (39) may perform a final scan to verify the accuracy of the plant and tray (16) to be removed. Any discrepancies are detected and corrected in real-time, ensuring that the tray is positioned exactly where intended.

[0184] When the Lifter AMR grips the tray (16) which is to be removed, the Transport AMR (with its Tray Trolley) would be instructed to move next to the Lifter AMR. The Lifter AMR (30) would then move its gripper (35) and tray (16) to the side of the Transport AMR (50) and place the tray (16) (with the plant) onto a holding area in the Tray Trolley. Once the tray with the plant is placed on the Tiered Trolley, the gripper (35) would release the tray (and the plant in it). In the meanwhile water which has been spilled or splashed onto the Tiered Trolley would run through the network of pipes in the Tiered Trolley into wastewater reservoir at the bottom of the Tiered Trolley.

[0185] Referring to Fig. 10D, the Tiered Trolley is also an integral component in the work of the Transport AMR and Lifter AMR. The Tiered Trolley (60) is equipped with a network of water pipes that run along each layer or shelf of the Tiered Trolley. These pipes are strategically placed to collect wastewater spilled from trays or containers holding plants or growing medium. Each tray or container on the trolley has drainage holes or channels designed to allow excess water to flow out into the pipes on the trolley. The water pipes on each layer of the trolley are angled or sloped towards a central collection point or drain at the base of the trolley. This design allows gravity to facilitate the flow of wastewater from the upper layers down towards the reservoir (61) at the bottom. At the base of the trolley, there is a reservoir or collection tank that stores the wastewater drained from the trays. This reservoir (61) may have a capacity suitable for holding large volumes of water to accommodate the drainage from multiple trays over an extended period.

[0186] The Lifter AMR would then move to the next location where another unhealthy plant would be removed from the rack and placed onto another holding area of the Tray Trolley (on the Transport AMR).

[0187] Fig. 11 is a perspective view of the UAV, Transport AMR with a Tiered Trolley after the Tiered Trolley had received a tray from the Lifter AMR and the Transport Robot with the Tiered Trolley on it is about to move off after completion of a transfer of Trays from the Racking System to the Tiered Trolley.

[0188] It may be seen that the UAV, Lifter AMR and Transport AMR may work together to carry out maintenance tasks within the Vertical Farm. Such maintenance tasks would include moving plants from one rack to another (in accordance to its growth), cleaning and clearing debris and dead leaves.

[0189] The UAV would fly through a designated aisle and images and data obtained during its flight transmitted to the Computer Servers. Such data would include picking up various particles in the air such as lightweight Volatile Organic Compound (VOC) sensors to detect specific levels of Terpenes, Formaldehyde, Benzene, Toluene, Xylene, Ethylene, Methane, Acetone, Ethanol, and Isopropanol and detecting harmful ranges. Images obtained by the UAV would also be analyzed by the Computer System, which would then plan action to be taken by the Transport AMR and Lifter AMR.

[0190] The action plan would be programmed by the Computer Server and relayed to Lifter AMR and Transport AMR, together with detailed routes and tasks to be performed. The Lifter AMR and Transport AMR would then navigate the farm efficiently, executing tasks like removal or trays and changing position of the trays for maintenance work. This synergistic collaboration, driven by artificial intelligence, not only maximizes efficiency but revolutionizes the way we cultivate crops indoors, ushering in a new era of smart and sustainable agriculture.

[0191] The movements of the Transport AMR and Lifter AMR are programmed by the Computer System. Each Transport AMR and Lifter AMR may work independently of each other or may work together, based on the work plans for each AMR, which again are planned based on earlier data transmission of condition of the plants, as collated by the UAVs during its flight routines. Based on the data collated and analyzed, a work plan is prepared for each Lifter AMR and Transport AMR.

[0192] Based on the work plan assigned, a Lifter AMR may be sent to a specific location in between aisles to pick up a tray of produce, which was detected to be in an unhealthy condition. ATransport AMR would follow behind. The Transport AMR would have a Tray Trolley placed on it.

[0193] The Lifter AMR has an extensible arm at the end of which is a gripper. On being guided to the precise location where the unhealthy plant is placed, the Lifter AMR would extend its arm to the height where the tray with the unhealthy plant is located. The gripper would be guided to the exact location of the unhealthy plant. The Lifter AMR would then be instructed to use its gripper to grip the tray in which the unhealthy plant is placed. The tray and the unhealthy plant (or plants, if more than one plant is confirmed to be unhealthy) is then brought down.

[0194] Retrieval of Trays and Plants

[0195] The Transport AMR would work in conjunction with the UAVs. Data collected by the UAVs are collated and analyzed by the computer system, which would store data obtained from each UAV on each plant in the Vertical Farming System, including such details as date of planting and history of plant growth. Each image of each plant maintained in the Vertical Farming System are analyzed and based on the assessment of the condition of each plant, action is taken. The action would be a set of instructions communicated to the Lifter AMR and Transport AMR, with detailed instructions on how to carry out each task.

[0196] The route of the Transport AMR and Lifter AMR would be programmed and then the AMRs sent out on its mission. The AMRs would also navigate in between the aisles of the Vertical Farm, being guided by the same real-time location 3-D coordinate system used in the Route Guidance System. .

[0197] Upon reaching the programmed section, the Lifter AMR would be extended to the height where the tray containing the plant is placed in. The Lifter AMR would extend its base to ensure the balance of the AMR is not adversely affected to ensure the AMR does not topple over. The robotic arm of the AMR would extend to grip the tray and plants in the tray and bring it to a designated area where manual operations on the plant may be carried out.

[0198] Embodiment of Vertical Farming System - Strawberry Flower Pollination by UAV

[0199] An embodiment of a Smart Autonomous Vertical Farming System for Strawberries and the process of strawberry flower pollination in this Smart Autonomous Vertical Farming System is illustrated with reference to Fig 12, Fig 13 and Fig 14.

[0200] Fig. 12 is a diagram of how the UAV navigates a part of a Strawberry Vertical Farming System showing the take off point.

[0201] Fig. 13 is an illustration of the types of images captured by the UAV as it flies along its programmed route, obtaining images of the plants placed on the racks in various sections (such as young seedlings in Zone 1 , young plants in Zone 2 and mature plants in Zone 3 of the Strawberry Vertical Farming System.

[0202] Fig. 14 is a diagram of how the UAV navigates a part of a Strawberry Vertical Farming System showing the UAV returning to its landing point.

[0203] Fig. 12 shows how the UAV navigates a part of a Strawberry Vertical Farming System from its assigned take off point. In the dynamic operation of a UAV within an indoor farm environment, the process begins with the UAV taking off from a designated spot, propelled into the air with precision and control. This launch initiates a sequence of events guided by user input regarding the sequence of zones (marked Zone 1 , Zone 2 and Zone 3) the drone should traverse. Upon receiving this input, the UAV's onboard system employs Dijkstra's shortest path algorithm, a sophisticated computational tool, to plan the most efficient trajectory through the designated zones. This algorithm utilizes the 3D positional coordinates of Fiducial Markers and Low Frequency Beacons meticulously placed throughout the farm to ensure autonomous precise navigation.

[0204] By integrating data from these tags with onboard sensors, the UAV accurately determines its position and orientation within the three-dimensional space of the farm. With this precise localization established, the UAV autonomously calculates and executes its flight path, seamlessly transitioning between zones while dynamically adjusting altitude and speed as needed. Throughout the mission, real-time monitoring and adjustments are made based on feedback from both the Low Frequency Beacon and onboard sensors, enabling the UAV to navigate with unparalleled accuracy and efficiency.

[0205] This integrated approach, combining user input, algorithmic planning, and Low Frequency Beacons navigation, empowers the UAV to fulfill its objectives effectively, whether it's monitoring crop health, surveying terrain, or performing other agricultural tasks with precision and autonomy.

[0206] In the intricate process of navigating up and down each tier within racks in a designated zone, the UAV's onboard camera employs sophisticated Al algorithms to conduct comprehensive analysis of the crops, particularly various types of lettuce. For the purpose of illustration, three different stages of plants are shown in Fig. 13, for example young seedlings would be in Zone 1 , young plants in Zone 2 and mature plants in Zone 3. As the UAV manoeuvres through the tiers, its camera captures detailed images of the plants, which are then processed by the Al algorithms to classify the different varieties accurately. Additionally, the algorithms estimate the surface areas of the leaves, providing valuable insights into the growth stage and health of the crops. By leveraging this data, the UAV can forecast the harvesting date, identifying when the crops are approaching maturity.

[0207] As the anticipated harvesting date draws near, the UAV seamlessly interacts with an Autonomous Mobile Robot (AMR), signalling it to initiate the automated delivery process. The UAV communicates the precise location of the trays containing the mature lettuce to the AMR, which then autonomously navigates to the specified racks. Utilizing its manipulation capabilities, the AMR carefully collects the trays and transports them to the trolley designated for harvesting. Furthermore, the UAV's advanced imaging capabilities extend beyond classification and growth estimation to disease detection. By analysing the visual characteristics of the crops captured by its onboard camera, the UAV can detect signs of disease or stress in the lettuce plants. This early detection enables proactive intervention, allowing farmers to implement targeted treatments or mitigation strategies to prevent the spread of disease and preserve crop health.

[0208] In summary, the integration of UAVs with Al algorithms and collaboration with AMRs revolutionizes the agricultural workflow, facilitating precise monitoring, forecasting, and automated harvesting processes. By harnessing the capabilities of these advanced technologies, Vertical Farm Operators can optimize crop management practices, enhance productivity, and ensure the sustainability of their operations.

[0209] In an innovative agricultural scenario, the UAV takes on the role of pollinator, assisting in strawberry flower pollination while also performing ripe detection and monitoring VOC (Volatile Organic Compound) gases. Equipped with specialized tools and sensors, the UAV autonomously navigates through the strawberry fields, scanning for ripe flowers with its advanced imaging system. Using Al algorithms, the UAV identifies flowers at the optimal stage for pollination, ensuring efficient fertilization and fruit development.

[0210] Simultaneously, the UAV's VOC sensors continuously monitor the levels of volatile organic compounds emitted by the plants. These sensors detect subtle changes in the chemical composition of the air surrounding the plants, providing valuable insights into plant health and stress levels. By analyzing VOC data in real-time, the UAV can identify potential issues such as disease outbreaks, nutrient deficiencies, or environmental stressors, enabling timely intervention to mitigate risks and optimize crop health.

[0211] As the UAV identifies ripe flowers for pollination, it carefully maneuvers to each flower, depositing pollen with precision to facilitate fertilization. This autonomous pollination process helps ensure consistent fruit set and yield for the strawberry crop, enhancing overall productivity and quality.

[0212] By combining pollination services with ripe detection and VOC sensing capabilities, the UAV offers a comprehensive solution for strawberry cultivation, providing growers with valuable data and assistance to optimize crop management practices and maximize harvest yields. This innovative approach underscores the potential of advanced technology to revolutionize agricultural processes and address challenges in food production sustainability.

[0213] Referring to Fig. 14, upon completion of its assigned tasks, the UAV would navigate and return to its assigned landing point (which in Fig. 12 is the original takeoff point).

[0214] Upon the conclusion of its tasks within an indoor environment, the UAV initiates its return journey to the designated home point for landing, employing 3D positional coordinates provided by indoor Low Frequency Bandwidth beacon tags for precise navigation. These beacon tags serve as reference points throughout the environment, emitting signals that the UAV's onboard navigation system detects and triangulates to determine its precise position in three-dimensional space.

[0215] With the aid of these beacon tags, the UAV calculates the most efficient route back to the home point, considering factors such as obstacles, terrain, and airspace regulations. As it ascends to a safe altitude, the UAV begins its homeward journey, autonomously following the pre-calculated path with precision.

[0216] Fig. 13 shows how various types of images are captured by the UAV as it flies along its programmed route. The UAV obtains images of the plants placed on the racks in various sections of the Strawberry Vertical Farming System. Throughout the flight, the UAV continually adjusts its trajectory based on real-time feedback from the beacon tags, ensuring accurate positioning and navigation. As it approaches the home point, the UAV utilizes visual cues (landmarks, fiducial markers) and additional sensors (wireless bacons) to further refine its position, guaranteeing pinpoint accuracy for the landing.

[0217] Fig. 14 shows the UAV returning to its landing point. Finally, with a controlled descent, the UAV gracefully lands at the designated home point, completing its mission safely and efficiently. By leveraging the 3D positional coordinates provided by indoor wireless beacon and fiducial markers, the UAV can autonomously navigate through indoor environments with confidence, enabling precise landings and facilitating seamless transitions between flight missions. This capability enhances operational efficiency and safety, making indoor UAV operations more reliable and effective. Use of Artificial Intelliqence for Smart Autonomous Vertical Farminq

[0218] In order to daily manage the indoors Vertical Farm on a smart autonomous manner, reliance is placed on use of Artificial Intelligence as massive amounts of raw data have to be handled and analyzed in order for work instructions to be issued to each UAV and AMR Lifter and AMR Transport, each with details of route to be taken in order to execute these work instructions. Crop monitoring using Al algorithms are employed to compare images of plants with plant diseases and deficiency conditions with images of know plant diseases and deficiency conditions to arrive at a diagnosis of the condition plant, as well as recommending corrective action.

[0219] The corrective action including the dosage of plant nutrients and supplements are worked out by the computer system and instructions for preparation and supply of these plant nutrients and supplements planned and made. These steps are known in the art and not discussed further.

[0220] Use of Artificial Intelliqence for route planninq in Smart Autonomous Vertical Farm operations

[0221] The Smart Autonomous Vertical Farming System is build on use of Artificial Intelligence algorithms which forms the foundation for the Real-Time Locating System used in an UAV of the invention is used for the AMR in route planning.

[0222] Even for simple manual operations to the more complex operations in the Vertical Farming System, Al is used. For example, in the process of daily planning of tasks. In order to plan the day’s tasks, algorithms are used to compare the images collected from each plant, in order to produce a series of tasks for the next day. The Computer Planning and Control System would then work out the tasks to be assigned, select an AMR for the task, plan the route to be taken using the Route Guidance System. The tasks to be executed are also continuously transmitted back in real time to the Planning and Control System, to enable the Computer to issue adjustments in instructions depending on the images transmitted by Real Time Locating System.

[0223] Throughout this process, precise control and coordination of the AMR’s tasks and movements would ensure smooth and accurate execution of the assigned tasks. The AMR would likewise use the Real-Time Locating System to confirm its position when it reaches a point in the route for it to carry out its tray retrieval and tray placement operations. An illustration of Shortest Path Planning using Dijkshtra’s Algorithm is shown herein:-

[0224] Shortest Path Planning

[0225] Another illustration of another algorithm for triangulation is shown below:-

[0226] UWB Triangulation Algorithm 3D Triangulation algorithm using 4 anchors & 1 tag z = (r-r42+ z42) / 2z4

[0227] Smart Autonomous Operations in Vertical Farms

[0228] The backbone of the Smart Autonomous Farming System is based on integration of Artificial Intelligence (Al), algorithms, data gathering, processing and data management all of which are managed and processed by the Planning and Control Systems, Route Guidance System and Library of Reference Data and database in the computer servers.

[0229] (a) Integrated Planning and Control System:

[0230] The integrated Planning and Control computerized system also coordinates the daily operations of UAVs, AMRs, and Tiered Trolleys within the vertical farm. It manages tasks such as scheduling UAV flights for monitoring and maintenance, optimizing AMR routes for harvesting and transportation, and monitoring trolley movements and environmental conditions. The integration and control computer system may utilize loT (Internet of Things) technology, Al algorithms, and cloud-based platforms for real-time monitoring, data analysis, and decision-making. The same computer system operates the UAVs and AMRs and other systems of the Smart Vertical Farming System such as lighting system, irrigation system and electrical systems. The integrated Planning and Control computer server and related systems including the Route Guidance System and Database and Library are essential for the organisation of daily work programmes for each AMR and UAV in the Smart Vertical Farming System.

[0231] (b) Use of Algorithms

[0232] Smart operations to run, manage, plan and organise daily activities in the Smart Vertical Farming System using the integrated Planning and Control computerized system and algorithms is essential for execution of daily tasks.

[0233] (c) Landmarks

[0234] In the context of the Vertical Farming System, a landmark refers to a distinct and recognizable feature within the Vertical Farming environment that serves as a reference point for navigation, localization, and mapping. Landmarks are typically identifiable objects or structures that are easily detected and distinguished by the robot’s sensors. Landmarks as described in this invention would also refer to Wireless Beacons placed in the racking system and other 3D Positional Tags such as fiducial markers placed in the Vertical Farm. The totality of landmarks and 3D Positional Tags form the basis of the network of routes managed by the Route Guidance System.

[0235] (d) Route Guidance System and route planning

[0236] The Route Guidance System is also an essential part of the Smart Vertical Farming System. The Route Guidance System maintained in the computer server would contain latest landmarks and 3D Positional Tags. The computer server would also work out detailed flight paths for the UAVs and route maps for the AMRs so that all activities planned are implemented and further feedback obtained and used for the next day’s work programme for the Vertical Farming System.

[0237] (e) Daily monitoring of health and other conditions of plants

[0238] The computer server also contains database of images of many types of plant diseases which would serve as a reference to the review of images of the plants captured by the UAVs. Algorithms are used to compare images taken by the UAVs with historical data and reference data to detect crop diseases, monitor crop growth and estimate harvesting date based on leaf surface area.

[0239] Image and other data obtained daily by the UAVs are instantly updated to the Computer System, analysed and program planned by the computers for the next day. These daily activities are then programmed into each AMR. The program would include instructions for activities to be undertaken as well as route to be taken using a UAV or AMR based on the route guidance system.

[0240] ADVANTAGEOUS EFFECTS OF THE INVENTION

[0241] The present invention proposes a Smart Autonomous Vertical Farming System using AMRs and UAVs to fully manage the daily operations of a Vertical Farming System. The UAVs and AMRs uses Artificial Intelligence to transmit operational data, plant growth data, plant condition data to a central server, where the data is analyzed and instructions for the management of plant conditions and plant management including growing, attending to diseases, mineral and other deficiencies, water supply and conditions are given to the UAVs and AMRs. The UAVs and AMRs would then attend to the instructions issued by the computer servers and travel to the location of the plant and attend to the condition. The smart autonomous Vertical Farming System would reduce use of both skilled and unskilled manpower, which is a big component of operational costs of a Vertical Farming System. It also improves the maintenance capability required for such Vertical Farms.

[0242] A Vertical Farming System which is smart Reduces manpower and improve productivity Uses Technology which is the state of the Art Truly innovative with better crop quality

Claims

CLAIMS1. A Smart Autonomous Vertical Farming System in an indoor farm comprising a Computer Server, a Planning and Control System, a Library containing images and reference data and a plurality of wireless routers for data transmission to and from all parts of the indoor farm, characterized in that the indoor farm is provided with a plurality of 3D positioning tags, wireless beacons and designated landmarks to form a plurality of routes for movement inside and through the Vertical Farming System, and the indoor farm is provided with a plurality of racks which are spaced apart forming an aisle between each rack, and each rack is also spaced apart vertically from the rack above and the rack below, with a plurality of trays of plants placed on the racks, said plants growing with indoor lightings and an irrigation system is employed to nourish the said plants with water and nutrients, wherein new data are gathered by a plurality of Autonomous Mobile Robots (AMR), said data including images of the plants, health condition and growth status of the plants obtained from the AM Rs, said data transmitted through the wireless routers to the Computer Server, wherein the Computer Server schedules daily operations for the AMRs and maintenance works to be carried out by the AMRs autonomously. wherein said daily operations and maintenance works to plant, grow, tend and harvest the plants are autonomously transmitted to each AMR through the wireless routers by the Computer Server, wherein the 3D positioning tags, wireless beacons and designated landmarks in the indoor farm form a Route Guidance System, for movements of the AMRs for said daily operations and maintenance within the Vertical Farming System, and said movements are planned and executed using the Route Guidance System.

2. The Smart Autonomous Vertical Farming System of Claim 1 , wherein the images of the plants are transmitted to the Computer server and are compared with those in a database on growth and health conditions of the plant as well as historical data of the plants kept in the Computer Server, to determine the best mix of nutrients and the indoor lightings for optimum growth of the plants.

3. The Smart Autonomous Vertical Farming System of Claim 1 , wherein instructions for the daily operations and maintenance are worked out by the Computer Server and are transmitted to each AMR, said instructions including tasks to be performed, the route within the indoor farm to be taken by each AMR to carry out the daily operations and maintenance works.

4. The Smart Autonomous Vertical Farming System of Claim 1 , wherein the plurality of 3D positioning tags, wireless beacons and designated landmarks in the indoor farm form the basis of a Route Guidance System, and data obtained daily from movements of the AMRs are updated to the Route Guidance System.

5. The Smart Autonomous Vertical Farming System of Claim 3, wherein the route and the instructions to the AMR include sensing the designated landmarks, 3D Positional Tags and Wireless Beacons, matching the images of designated landmarks and 3D positional Tags with the instructions issued to each AMRs so as to navigate from one location to another within the indoor farm.

6. The Smart Autonomous Vertical Farming System of Claim 1 , wherein the AMRs include:-An Unmanned Aerial Vehicles (UAV), modified to fly indoors;An Autonomous Mobile Robot Lifter (“AMR Lifter”);An Autonomous Mobile Robot Transporter (“AMR Transporter); andATiered Trolley placed on the AMR Transporter.

7. The Smart Autonomous Vertical Farming System of Claim 6, wherein the AMR Transporter and the AMR Lifter having a chargerand the AMR Lifter and the AMR Transporter are connectable to a mobile charger when both are not in use.

8. The AMR Lifter as claimed in Claim 6, wherein each AMR Lifter is equipped with a Realtime triangulation locating system comprising a plurality of cameras and sensors and a wirelessdata transmitter to enable the AMR Lifter to move in the indoor farm, using the sensors and the cameras to locate the 3D positional tags and landmarks in the indoor farm, sending the data including the position and the location thereof to the Computer Server, and receiving further instructions and data to enable the AMR Lifter to execute tasks along the aisles and to locate the position of trays and other objects for performance of task assigned.

9. The UAV as claimed in Claim 6, wherein the UAV is adapted to fly indoors by using the 3D Positional Tags, Low Frequency Beacons and designated landmarks to fly from one location to another location within the indoor farm, and wherein the 3D Positional Tags, Low Frequency Beacons and designated landmarks are used for route guidance and navigation, on the basis of the daily maintenance work planned by the Computer Server and changes in each flight route updated to the Route Guidance System. .

10. The UAV as claimed in Claim 6, wherein the UAVs are equipped with an extensible probe with a plurality of sensors including VOC sensor to detect air conditions including specific levels of Terpenes, Formaldehyde, Benzene, Toluene, Xylene, Ethylene, Methane, Acetone, Ethanol, and Isopropanol ranging around the plants in the racks.

11. The AMR Lifter as claimed in Claim 6 wherein it is equipped with a plurality of cameras, sensors and wireless data transmitter to enable movement of AMR Lifter in the indoor farm, using the sensors and the cameras to locate 3D positional tags and designated landmarks in the indoor farm, and wherein the wireless data transmitter sends data including the position and the location thereof to the Computer Server, and receives further instructions to enable movement of AMR Lifter along the aisles and for performance of task assigned.

12. The AMR Lifter as claimed in Claim 6, wherein the AMR Lifter is provided with a flexible extensible arm with a gripper device at one end thereof to grip and to remove the trays.

13. The AMR Lifter as claimed in Claim 6 wherein it is provided with a camera at the end of the flexible extensible arm to guide the flexible extensible arm with the gripper device to grip the trays and also to release its grip in order to move the trays from one position to another.

14. The AMR Lifter as claimed in Claim 11 , wherein the camera is used with the Real Time Locating System by the AMR Lifter to guide the flexible extensible arm with the gripper device to grip and place or remove the trays on the racks.

15. The AMR Lifter as claimed in Claim 11 , wherein the AMR Lifter is provided with a camera at the end of the flexible extensible arm thereof to enable the AMR Lifter to use its Real Time Locating System to triangulate its position relative to the 3D Positional Tags and designated landmarks and computes the necessary adjustments to ensure precise alignment, to commence gripping or release of trays.

16. The AMR Lifter as claimed in Claim 13 wherein the AMR Lifter removes the trays with plants therein from one rack and places the trays onto the AMR Tiered Trolley, which would transport these trays and placed these trays onto another rack in another location, as part of the growing program of these plants.

17. The AMR Lifter and the AMR Tiered Trolley as claimed in Claim 6 which works together to remove and / or to replace the trays from one location to another location within the vertical farming system.

18. The Tiered Trolley as claimed in Claim 6 wherein it has a network of water pipes that run along each layer or the shelf of the Tiered Trolley to a reservoir at the base thereof to collect wastewater spilled from the trays, so that the spilled wastewater is collected in the reservoir.

19. The Route Guidance System as claimed in Claim 1 , which has a plurality of 3D Positional Tags and designated landmarks in the indoors farm to form a route network for movement of the UAVs and AMRs, and details or changes in said route network obtained from previous flights or movements of the UAVs and AMRs are updated to the Route Guidance System in the Computer Server.

20. The Route Guidance System of Claim 17, wherein the Computer Server works out a route for each AMR Lifter and AMR Transporter to take whenever the AMR Lifter and AMR Transporter are instructed to carry out a series of tasks, and wherein such routes are constantly updated when the AMR Lifter and AMR Transporter transmits data of the surrounding thereof.21 . The Route Guidance System as claimed in Claim 18, wherein the Computer Server works out a route for the UAV to undertake whenever the UAV is assigned to carry out a series of tasks, and the route is constantly updated when the UAV transmits data of the surrounding thereof.

22. The UAV as claimed in any of the preceding Claims wherein the data obtained from the sensors and / or cameras of the UAV are transmitted to the Computer Server for analysis of the condition of health and status of the plants by matching the new data against the stored historical data of the plants and checking against the library on health and other conditions of these plants .

23. The designated landmarks used in the Smart Autonomous Vertical Farming System as claimed in any of the preceding Claims include landmarks in the vertical farm which are designated as landmarks in the Route Guidance System, wireless beacons and Fiducial markers placed in the indoors farm.

Images