The present disclosure relates generally to automatic smart monitoring and control system for hydroponic farms; and more specifically, to a system and method for automation and control of at least one hydroponic farm using Internet of Things (IoT) and Artificial Intelligence (AI).
BACKGROUND
In the recent past, newer and modern agricultural and horticultural techniques are being employed to grow plants. One such modern horticulture technique is hydroponics. Generally, the hydroponic systems are used to grow fruit bearing smaller plants without soil using a nutrient solution in an aqueous solvent. In the hydroponic systems, the roots are exposed to the nutrient solution for the uptake of nutrients by the plant. The roots are generally supported in mediums such as coco-peat, perlite etc. However, there are a lot of drawbacks associated with the conventional hydroponic systems. In the conventional hydroponic systems, it is difficult to maintain the composition of nutrient solution creating imbalance of nutrients in the nutrient solution which may result in poor growth and health of the plant. Moreover, there is a lot less control on the chemical properties of the nutrient solution such as pH, electrical conductivity etc. which may adversely affect the growth of the plant. Furthermore, the control over the physical properties of the nutrient solution such as temperature, dissolved oxygen etc. requires a lot of energy input.
Moreover, the conventional hydroponic systems are difficult to monitor. Furthermore, the conventional hydroponics systems lack granular-level control on the different parameters such as physical and chemical properties of the nutrient solution and environmental parameters. Furthermore, the conventional hydroponic systems lack the capabilities of predictive and preventive actions to regulate the various parameters in the hydroponic system.

Therefore, in light of the foregoing discussion, there exist a need to at least partially solve the above-mentioned problems by providing a system for automation and control of hydroponic farms.
SUMMARY
The present disclosure seeks to provide a system and method for automation and control of hydroponic farms.
The object of the present disclosure is to provide a system and method for automation and control of hydroponic farms that overcomes at least partially the problems encountered in the prior art.
In one aspect, an embodiment of the present disclosure describes a system for automation and control of at least one hydroponic farm, the system comprising an on-site master controller communicably coupled to a server arrangement, an artificial intelligence module and a plurality of sensor slaves, the on-site master controller is configured to:
- receive a sensor data from the plurality of sensor slaves; and
- regulate at least one environmental parameter of the at least one hydroponic farm, characterized in that the artificial intelligence module is configured to provide at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm.
The present disclosure is advantageous in terms of providing energy-efficient, cost-efficient and granular-level predictive control over the plant growth parameters in the at least one hydroponic farm.
Optionally, the on-site master controller comprises a slave configuration module, a data logging and analysis module, an on-site admin module, a data synchronisation module and an on-site database.
Optionally, the slave configuration module is configured to register the plurality of sensor slaves, receive a flow of the sensor data from the plurality of sensor slaves,

and communicate triggers generated by the on-site master controller for the regulation of the at least one environmental parameter in the hydroponic farm.
Optionally, the data logging and analysis module is configured to log the received sensor data in the on-site database for monitoring of the at least one hydroponic farm, and filter, segregate and process the received sensor data for data analysis.
Optionally, the on-site admin module is configured to receive input from an on-site user for the regulation of the at least one environmental parameter in the hydroponic farm.
Optionally, the data synchronisation module is configured to send the processed sensor data to the server arrangement and the artificial intelligence module, and receive communication from the server arrangement and the artificial intelligence module.
Optionally, the on-site database is configured to store the logged sensor data, the processed sensor data, the inputs received from the on-site user, the triggers generated by the on-site master controller, the communication received from the server arrangement and the communication received from the artificial intelligence module.
Optionally, the server arrangement comprises a farm registration module, a super admin control module, a remote monitoring and configuration module, and a database arrangement.
Optionally, the farm registration module is configured to register at least one hydroponic farm with the super admin control module.
Optionally, the super admin control module is configured to generate authentication details of the at least one registered hydroponic farm for the on-site admin module and the remote monitoring and configuration module.
Optionally, the super admin control module is configured to receive input from a super admin for the regulation of the at least one environmental parameter in the hydroponic farm.

Optionally, the remote monitoring and configuration module is configured to present the processed sensor data to a remote user and receive input from the remote user for the regulation of the at least one environmental parameter in the hydroponic farm.
Optionally, the server arrangement is configured to receive the processed sensor data from the data synchronisation module and communicate the inputs received from the remote user and the super admin to the on-site master controller via the data synchronisation module.
Optionally, the database arrangement is configured to store the registered hydroponic farm details, the generated authentication details, the inputs received from the super admin, the inputs received from the remote user, and the processed sensor data received from the data synchronisation module.
Optionally, the plurality of sensor slaves includes motor controller slave, water level sensor slave, ambient temperature sensor slave, ambient humidity sensor slave, pH sensor slave, electrical conductivity sensor slave, flow sensor slave, dissolved oxygen sensor slave, Photosynthetically Active Radiation sensor slave, air flow sensor slave, CO2 sensor slave, root zone temperature sensor slave and root zone humidity sensor slave.
Optionally, the on-site master controller is configured to generate the triggers to regulate the at least one environmental parameter based on the at least one of:
- the processed sensor data generated by the processing of the received sensor data from the plurality of sensor slaves;
- the inputs from the on-site user via the on-site admin module;
- the communication received from the artificial intelligence module; and
- the communication received from the server arrangement.
Optionally, the generated triggers actuate at least one actuator from a plurality of actuators to regulate the at least one environmental parameter.
Optionally, the artificial intelligence module comprises a predictive climate control module, a disease analysis module and a predictive harvesting module.

Optionally, the predictive climate control module is configured to receive the processed sensor data to predict the at least one environmental parameter and communicate a generated instruction to the on-site master controller for the predictive climate control by the regulation of the at least one environmental parameter in the hydroponic farm.
Optionally, the disease analysis module is coupled with a plurality of hyperspectral imaging modules and configured to receive the processed sensor data and a hyperspectral imaging data to predict occurrence of a disease and communicate a generated instruction to the on-site master controller for a disease mitigating action.
Optionally, the predictive harvesting module is coupled with the plurality of hyperspectral imaging modules and configured to receive the processed sensor data and a hyperspectral imaging data to predict a harvesting timeline and a quality of harvest.
In another aspect, an embodiment of the present disclosure provides a method for automating and controlling at least one hydroponic farm, the method comprising:
- receiving sensor data from a plurality of sensor slaves; and
- regulating at least one environmental parameter of the at least one hydroponic farm using an on-site master controller, wherein the on-site master controller is communicably coupled to a server arrangement, an artificial intelligence module and a plurality of sensor slaves,
characterized in that the method comprises providing at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm, using the artificial intelligence module.
Embodiments of the present disclosure substantially eliminate or at least partially address the aforementioned problems in the prior art, and enables granular-level predictive control over the plant growth parameters in the at least one hydroponic farm.

Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments construed in conjunction with the appended claims that follow.
It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 is a schematic illustration of a system for automation and control of at least one hydroponic farm, in accordance with an embodiment of the present disclosure.
FIG. 2 is a schematic illustrations of an on-site master controller, in accordance with an embodiment of the present disclosure.
FIG. 3 is a schematic illustration of a server arrangement, in accordance with an embodiment of the present disclosure.
FIG. 4 is a schematic illustration of an artificial intelligence module, in accordance with an embodiment of the present disclosure.
FIG. 5 is a schematic illustration of a method for automating and controlling at least one hydroponic farm, in accordance with an embodiment of the present disclosure.

In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION OF EMBODIMENTS
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. The terms "having", "comprising", "including", and variations thereof signify the presence of a component.
Referring to Fig. 1, there is illustrated a block diagram of a system 100 for automation and control of at least one hydroponic farm, in accordance with an embodiment of the present disclosure. The system 100 comprises an on-site master controller 102 communicably coupled to a server arrangement 106, an artificial intelligence module 104 and a plurality of sensor slaves 110, 112, 114, 116. The on-site master controller 102 of the system 100 is configured to receive a sensor data from the plurality of sensor slaves 110,112, 114,116 and regulate at least one environmental parameter of the at least one hydroponic farm. The artificial intelligence module 104 of the system 100 is configured to provide at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm.

The system 100 of the present invention would be advantageous in terms of providing a completely remote access and control of all the parameters of the hydroponic farm. Further, the system 100 of the present invention would be advantageous in terms of enabling lower operational costs and better scalability by enabling automated granular-level control of the parameters of the hydroponic farm. Advantageously, the system 100 is capable of integrating and handling a large number of farms. Advantageously, the system 100 of the present invention is capable of monitoring the health of all the devices installed in the hydroponic farm and predicting the requirements of maintenance of the devices installed in the hydroponic farm. Beneficially, the system 100 is capable of raising alerts and notifications on the malfunction of devices installed in the hydroponic farm. The system 100 of the present invention would be advantageous in terms of providing healthy growth to the plants in the hydroponic farm. Advantageously, the system 100 of the present invention maintains composition of nutrients in real time for the proper nutrition of the plant, resulting in healthy plant growth. The system 100 of the present invention extensively supports healthy root growth by maintaining optimal growth parameters around the roots. Advantageously, the system 100 of the present invention maintains the optimum physical and chemical properties of the nutrient solution and other environmental parameters for the healthy growth of the plants in the hydroponic farm.
Throughout the present disclosure, the term "system" refers to an integrated hydroponic system with a plurality of components such as IoT devices and artificial intelligence for automation and control of the at least one hydroponic farm in the system 100.
Throughout the present disclosure, the term "parameter" and "environmental parameter" are used interchangeably. The term "parameter" refers to physical, chemical and environmental characteristics or attributes required for optimum growth of the plant in the at least one hydroponic farm of the system 100. Optionally, the parameter may include but not limited to at least one of temperature, humidity, pH, electrical conductivity, dissolved oxygen, total dissolved solid

(TDS), CO2 level, water level, flow rate, photo illumination, air flow, root zone temperature, root zone humidity and so on. It would be appreciated that the plants growing in the at least one hydroponic farm may require different parameters according to their size, volume and stage of growth such as germination, vegetative, budding, flowering, fruiting etc.
Referring to Fig. 2, there is illustrated a block diagram of the on-site master controller 102 of the system 100, in accordance with an embodiment of the present disclosure. Throughout the present disclosure, the term "on-site master controller" refers to a specialised processing device installed on the farm configured to receive and process sensor data to generate actuator operating triggers based on the sensor data and communication received from other on-site and remote modules of the system 100. The on-site master controller 102 may be a Linux based custom System on Chip (SoC). In an example, the on-site master controller 102 may include an ARM architecture based custom Quad core Cortex-A72 processor clocked @1.5GHz, coupled with 8GB LPDDR4-3200 SDRAM and 256GB SSD. The on-site master controller 102 may include wireless connectivity options such as Wi-Fi, Bluetooth and so on. The on-site master controller 102 may include ports such as USB, Ethernet, HDMI, RS485 and so on for wired connections with other devices installed in the farm.
The on-site master controller 102 comprises a slave configuration module 204, a data logging and analysis module 206, an on-site admin module 208, a data synchronisation module 210 and an on-site database 212.
Optionally, the slave configuration module 204 is configured to register the plurality of sensor slaves 110,112,114,116, receive a flow of the sensor data from the plurality of sensor slaves 110, 112, 114, 116, and communicate triggers generated by the on-site master controller 102 for the regulation of the at least one environmental parameter in the hydroponic farm. The slave configuration module 204 may register new sensor slaves to receive a flow of sensor data from the new sensor slaves.

Optionally, the data logging and analysis module 206 is configured to log the received sensor data in the on-site database 212 for monitoring of the at least one hydroponic farm, and filter, segregate and process the received sensor data for data analysis. The data analysis is performed to monitor the performance of the various parameters in the at least one hydroponic farm. The data logging and analysis module 206 may segregate data from different sensors and analyse data using various mechanisms to present the data in any required form.
Optionally, the on-site admin module 208 is configured to receive input from an on-site user for the regulation of the at least one environmental parameter in the hydroponic farm. The on-site admin module 208 may include a web-based user interface to receive the super admin inputs and to present all the data to the super admin for the monitoring purpose. The on-site admin module 208 may display instructions generated by the artificial intelligence module 104 to the on-site user for receiving feedback on the instructions generated by the artificial intelligence module 104. The on-site admin module 208 may receive feedback from the on-site user regarding the instructions generated by the artificial intelligence module 104. The on-site user may be a local farm manager.
Optionally, the data synchronisation module 210 is configured to send the processed sensor data to the server arrangement 106 and the artificial intelligence module 104, and receives back communication from the server arrangement 106 and the artificial intelligence module 104.
Optionally, the on-site database 212 is configured to store the logged sensor data, the processed sensor data, the inputs received from the on-site user, the triggers generated by the on-site master controller 102, the communication received from the server arrangement 106 and the communication received from the artificial intelligence module 104. The on-site database 212 may be a non-transitory computer readable storage medium such as solid-state drive, hard disk drive and so on. The on-site database 212 may store the received data in structured or un-structured form or a combination thereof.

Optionally, the on-site master controller 102 comprises a root zone control module, wherein the root zone control module is configured to specifically maintain the root zone parameters. The root zone control module may be a specialized hardware designed for the specific purpose such that it may enable faster and more granular-level control of the root zone parameters.
Throughout the present disclosure, the term "plurality of sensor slaves" refer to specialised hardware installed with a sensor configured to perform defined tasks, for example, record readings from sensor at regular intervals and filter the raw sensor readings to provide useful information. The plurality of sensor slaves 110, 112,114,116 may be a low powered custom processing unit capable of specialized function such as recording and filtering sensor data. In an example, the plurality of sensor slaves 110, 112, 114, 116 may be an ARM based custom Cortex M0+ processing unit with particular sensors or an AVR 8bit Controller. The plurality of sensor slaves 110, 112, 114, 116 may include connectivity options such as Wi-Fi, Bluetooth and so on. Optionally, the plurality of sensor slaves 110, 112, 114, 116 may include hardware peripherals such as 16bit ADC, 16bit Timers, I2C, SPI and so on. Optionally, the plurality of sensor slaves 110, 112, 114, 116 may include a battery or may be connected to mains for power supply. The plurality of sensor slaves 110, 112, 114, 116 are communicably coupled with the on-site master controller 102 via the slave configuration module 204. Optionally, the plurality of sensor slaves 110, 112, 114, 116 may be coupled with the on-site master controller 102 in a mesh network topology, wherein the plurality of sensor slaves 110, 112, 114, 116 are connected directly, dynamically and non-hierarchically to communicate with the on-site master controller 102. Beneficially, the mesh network topology increases the fault tolerance of the system 100 and reduces the overall maintenance cost. Beneficially, the plurality of sensor slaves 110, 112, 114, 116 enable accurate and granular-level readings of the sensor data. Beneficially, the plurality of sensor slaves 110, 112, 114, 116 may act as a sub-controller for the sensors, and may configure and calibrate sensors.

Optionally, the plurality of sensor slaves 110, 112, 114, 116 includes but may not limited to motor controller slave, water level sensor slave, ambient temperature sensor slave, ambient humidity sensor slave, pH sensor slave, electrical conductivity sensor slave, flow sensor slave, dissolved oxygen sensor slave, Photosynthetically Active Radiation sensor slave, air flow sensor slave, CO2 sensor slave, root zone temperature sensor slave and root zone humidity sensor slave.
Optionally, the motor controller slave includes a 24-channel motor control unit wherein each channel is capable of handling 5 KW of single-phase load. Furthermore, every channel will have a current sensor as a feedback. The slave can execute on/off command received from the on-site master controller 102. Furthermore, the slave includes variable power control to make the system more power conservative.
Optionally, the water level sensor slave includes a waterproof ultrasonic based distance sensor. The water level sensor slave requires water level resolution of 5%.
Optionally, the ambient temperature sensor slave and the ambient humidity sensor slave include high accuracy temperature and humidity sensors.
Optionally, the pH sensor slave includes a potentiometric pH meter that measure the voltage between two electrodes and display the result converted into corresponding pH value. The potentiometric pH meter comprises a simple electronic amplifier and a pair of electrodes, or alternatively a combination electrode, and some form of display calibrated in pH units. The potentiometric pH meter usually has a glass electrode and a reference electrode, or a combination electrode. The electrodes, or probes, are inserted into the solution to be tested and the data is sent to the on-site master controller 102.
Optionally, the electrical conductivity sensor slave includes a specially designed low-cost electrical conductivity sensor.
Optionally, the flow sensor slave includes an ultrasonic flow sensor or a capacitive flow sensor to determine a rate of irrigation.

Optionally, the dissolved oxygen sensor slave includes a low-cost dissolved oxygen sensor to measure dissolved oxygen in the irrigation water.
Optionally, the air flow sensor slave includes a directional anemometer to measure the amount of airflow along with the direction of the air flow.
Optionally, the CO2 sensor slave includes a sensor which is highly sensitive to CO2 and less sensitive to alcohol and CO to determine the CO2 emission by the plants during night.
Optionally, the Photosynthetically Active Radiation sensor slave includes Quantum Light sensors that are calibrated to display PPFD (Photosynthetic Photon Flux Density) to measure the amount of light useful for the photosynthesis.
Optionally, the root zone temperature sensor slave and the root zone humidity sensor slave include high accuracy, waterproof temperature and humidity sensors.
Referring to Fig. 3, there is illustrated a block diagram of the server arrangement 106 of the system 100, in accordance with an embodiment of the present disclosure. Throughout the present disclosure, the term "server arrangement" refers to an arrangement of one or more servers that includes one or more processors that performs various operations. Optionally, the server arrangement 106 includes any arrangement of physical or virtual computational entities capable of performing the various operations. Moreover, it will be appreciated that the server arrangement 106 can be implemented by way of a single hardware server. The server arrangement 106 can alternatively be implemented by way of a plurality of hardware servers operating in a parallel or distributed architecture. As an example, the server arrangement 106 may include components such as memory, a processor, a network adapter and the like, to store and process information pertaining to the document and to communicate the processed information to other computing components, for example, such as on-site controller 102 and artificial intelligence module 104 of the system 100. The server arrangement 106 comprises a farm registration module 308, a super admin control module 310, a remote monitoring and configuration module 312, and a database arrangement 314.

Optionally, the farm registration module 308 is configured to register at least one hydroponic farm with the super admin control module 310.
Optionally, the super admin control module 310 is configured to generate authentication details of the at least one registered hydroponic farm for the on-site admin module 208 and the remote monitoring and configuration module 312. The super admin control module 310 may decide authorization, access, hierarchy and controls of other users such as on-site user, remote user. The super admin control module 310 may deny access of the system 100 to other users.
Optionally, the super admin control module 310 is configured to receive input from a super admin for the regulation of the at least one environmental parameter in the hydroponic farm. The super admin control module 310 may include a web-based user interface to receive the super admin inputs and to present all the data to the super admin for the monitoring purpose.
Optionally, the remote monitoring and configuration module 312 is configured to present the processed sensor data to a remote user and receive input from the remote user for the regulation of the at least one environmental parameter in the hydroponic farm. The remote monitoring and configuration module 312 may include a web-based user interface to receive the inputs from the remote user and to present all the data to the remote user for the monitoring purpose. The remote user may be a farm owner.
Optionally, the server arrangement 106 is configured to receive the processed sensor data from the data synchronisation module 210 and communicate the inputs received from the remote user and the super admin to the on-site master controller 102 via the data synchronisation module 210.
Optionally, the database arrangement 314 is configured to store the registered hydroponic farm details, the generated authentication details, the inputs received from the super admin, the inputs received from the remote user, and the processed sensor data received from the data synchronisation module 210. The database arrangement 314 may be storage systems, such as, for example, a relational

database like IBM DB2 and Oracle 9. The database arrangement 314 may store the received data in structured or un-structured form or in a combination thereof.
Referring to Fig. 4, there is illustrated a block diagram of the artificial intelligence module 104 of the system 100, in accordance with an embodiment of the present disclosure. Throughout the present disclosure, the term "artificial intelligence module" refers to a specialized processing hardware with a plurality of processing elements and neural engines capable of running various artificial intelligence algorithms to generate at least one of a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm of the system 100. The artificial intelligence module 104 may employ algorithms such as naive bayes, decision tree, KNN to enable at least one of a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm of the system 100. Optionally, the artificial intelligence module 104 may employ other deep neural network algorithms to enable at least one of a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm of the system 100. The artificial intelligence module 104 may be trained using supervised learning techniques or unsupervised learning techniques or a combination thereof. Optionally, the artificial intelligence module 104 may be trained using the data stored in the on-site database 212 or using the data stored in the database arrangement 314 or using a combination thereof. The artificial intelligence module 104 comprises a predictive climate control module 406, a disease analysis module 408 and a predictive harvesting module 410.
Optionally, the predictive climate control module 406 is configured to receive the processed sensor data to predict the at least one environmental parameter and communicate a generated instruction to the on-site master controller 102 for the predictive climate control by the regulation of the at least one environmental parameter in the hydroponic farm. The predictive climate control module 406 may regulate the at least one environmental parameter in the hydroponic farm according to the varying requirements of the plants based on their growth stages. The predictive climate control module 406 is configured to predict upcoming deviations

or changes in the parameters by processing the real-time sensor data received from the plurality of sensor slaves 110, 112, 114, 116. The predictive climate control module 406 is configured to send instruction to the on-site master controller 102 for triggering action to mitigate the upcoming change in the parameter before the actual change in the parameter. In an example, the predictive climate control module 406 predicts that the ambient temperature is about to rise in the hydroponic farm and instructs the on-site master controller 102 to turn on fogger before the actual change in temperature. Beneficially, such process saves lot of power consumption in the hydroponic farm. Advantageously, such process saves lot of cost during the production of plant in the hydroponic farm.
Optionally, the disease analysis module 408 is coupled with a plurality of hyperspectral imaging modules and configured to receive the processed sensor data and a hyperspectral imaging data to predict occurrence of a disease and communicate a generated instruction to the on-site master controller 102 for a disease mitigating action. The disease analysis module 408 is configured to predict occurrence of disease in the farm by processing the combination of the real-time sensor data received from the plurality of sensor slaves 110, 112, 114, 116 and the hyperspectral imaging data such that the disease analysis module 408 identifies image patterns of diseases. The disease analysis module 408 is configured to send instruction to the on-site master controller 102 for triggering action to mitigate the risk of disease. Optionally, the disease mitigating action may include but not limited to spraying organic fertilizers, releasing antibiotics in the nutrient media, altering composition of the nutrient media, altering physical and chemical properties of the nutrient media, altering the environmental parameters in the hydroponic farm and so on. In an example, the disease analysis module 408 may detect a fungal infection in the roots of the plant based on the temperature sensor data and hyperspectral imaging data. On the detection of the fungal infection, the disease analysis module 408 may instruct the on-site master controller to alter the pH of the nutrient media flowing through the roots of the plants growing in the hydroponic farm. Beneficially, such process saves plants from disease resulting in a good harvest.

Moreover, such process is advantageous in terms of detecting and mitigating diseases in very early stage to reduce wastage of the harvest.
Optionally, the at least one hydroponic farm comprises a plurality of hyperspectral imaging modules placed at different strategic locations in the at least one hydroponic farm to capture visual data of a root part and a shoot part of the at least one plant growing in the at least one hydroponic farm.
Throughout the present disclosure, the term "hyperspectral imaging module" refers to at least one visual data capturing device for capturing images and/or visual data of the at least one plant growing in the at least one hydroponic farm. Optionally, the hyperspectral imaging module may capture the visual data in form of at least one still image. More optionally, the hyperspectral imaging module may capture the visual data in form of at least one video recording. Beneficially, the hyperspectral imaging enables detection of diseases in the at least one plant growing in the plant growth system 100 by employing different wavelengths of the electromagnetic spectrum to capture the visual data.
Throughout the present disclosure, the term "disease" refers to a condition when at least one of the nutrients is not available to at least one of the plants for its optimum growth or to a condition when the at least one plant is suffering from an infection of micro-organisms such as bacteria, virus, fungi and so on. The disease or nutrient deficiency may occur when an actual amount of the particular nutrient is less than the required amount of that nutrient in the nutrient media for the optimum growth of the at least one plant growing in the at least one hydroponic farm.
Optionally, the predictive harvesting module 410 is coupled with the plurality of hyperspectral imaging modules and configured to receive the processed sensor data and a hyperspectral imaging data to predict a harvesting timeline and a quality of harvest in the at least one hydroponic farm. The predictive harvesting module 410 may be configured to predict ripening of the harvest in the farm by processing the combination of the real-time sensor data received from the plurality of sensor slaves 110, 112, 114, 116 and the hyperspectral imaging data. The predictive harvesting

module 410 may be configured to grade and segregate the harvest based on the quality and ripening time. The predictive harvesting module 410 may be configured to predict ripening time of the harvest. Beneficially, such process is may be beneficial for logistics planning.
Optionally, the artificial intelligence module 104 monitors the health of the devices installed in the at least one hydroponic farm based on the received sensor data and other device parameters such as voltage, current, resistance and so on or a combination thereof. The artificial intelligence module 104 may predict occurrence of faults in the devices installed in the at least one hydroponic farm and notifies for predictive maintenance of fault prone devices. Beneficially, such predictive maintenance reduce downtime of the at least one hydroponic farm.
Optionally, the on-site master controller 102 is configured to generate the triggers to regulate the at least one environmental parameter based on the at least one of:
- the processed sensor data generated by the processing of the received sensor data from the plurality of sensor slaves 110, 112, 114, 116;
- the inputs from the on-site user via the on-site admin module 208;
- the communication received from the artificial intelligence module 104; and
- the communication received from the server arrangement 106.
The on-site master controller 102 may primarily generate the triggers based on the processed sensor data generated by the processing of the received sensor data from the plurality of sensor slaves 110, 112, 114, 116. The on-site master controller 102 may also generate triggers based on the inputs from the on-site user via the on-site admin module 208. The triggers based on the inputs from the on-site user via the on-site admin module 208 may override the triggers generated based on the processed sensor data. The on-site master controller 102 generate triggers based on the communication received from the artificial intelligence module 104. The on-site master controller 102 also generate triggers based on the communication received from the server arrangement 106. The communication received from the server arrangement 106 may include the inputs of the remote user or the super admin or a combination thereof. The generated triggers are communicated by the

on-site master controller 102 based on the time such that the latest trigger gets executed by the actuators.
Optionally, the generated triggers actuate at least one actuator from a plurality of actuators to regulate the at least one environmental parameter.
Throughout the present disclosure, the term "actuator" refers to any device which cause a machine or any other device to operate and result in some changes in the at least one hydroponic farm. Optionally, the at least one actuator may be fitted inside the at least one hydroponic farm to operate inside the at least one hydroponic farm for regulating various parameters inside the at least one hydroponic farm. Optionally, the at least one actuator may comprise at least one high pressure meter, at least one solenoid valve, at least one pressure gauge, at least one exhaust fan, at least one circulation fan, at least one fogger sprayer, sprinkler, irrigation motor cooling pad motor and so on or combination thereof.
The system 100 may employ various modes of communication to enable communication between the components of the system 100. In an example, Wi-Fi or Bluetooth or a combination thereof may be employed in Mesh topology for communication between the on-site master controller 102 and plurality of sensor slaves 110, 112, 114, 116. Optionally, Modbus may be employed for enabling communication between the digital devices in the system 100. Optionally, 2G or 4G broadband cellular network or a combination thereof may be employed for enabling communication between the on-site master controller 102 and server arrangement 106 via hypertext transfer protocol or internet protocol and so on. Optionally, I2C, Onewire, SPI, UART and so on technologies may be employed for communication between on-site peripherals or actuators.
Optionally, the system 100 comprises a renewable energy source (not shown in the Figs.) for supplying energy to the various modules and actuators installed in the at least one hydroponic farm. Optionally, the energy supplied may be in a form of electricity. Beneficially, the renewable energy source supplies electricity to the at least one hydroponic farm for the uninterrupted and independent operation of the

various modules and actuators installed in the at least one hydroponic farm. The renewable energy source may include but not limited to photovoltaic panels.
In another embodiment, the system 100 comprises an on-site master controller 102 communicably coupled to a server arrangement 106, an artificial intelligence module 104 and a plurality of sensor slaves 110,112, 114, 116. The on-site master controller 102 of the system 100 is configured to receive a sensor data from the plurality of sensor slaves 110,112,114,116 and regulate at least one environmental parameter of the at least one hydroponic farm. The artificial intelligence module 104 of the system 100 is configured to provide at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm. The on-site master controller 102 comprises a slave configuration module 204, a data logging and analysis module 206, an on-site admin module 208, a data synchronisation module 210 and an on-site database 212.
In yet another embodiment, the system 100 comprises an on-site master controller 102 communicably coupled to a server arrangement 106, an artificial intelligence module 104 and a plurality of sensor slaves 110,112, 114, 116. The on-site master controller 102 of the system 100 is configured to receive a sensor data from the plurality of sensor slaves 110,112,114,116 and regulate at least one environmental parameter of the at least one hydroponic farm. The artificial intelligence module 104 of the system 100 is configured to provide at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm. The server arrangement 106 comprises a farm registration module 308, a super admin control module 310, a remote monitoring and configuration module 312, and a database arrangement 314.
In yet another embodiment, the system 100 comprises an on-site master controller 102 communicably coupled to a server arrangement 106, an artificial intelligence module 104 and a plurality of sensor slaves 110,112, 114, 116. The on-site master controller 102 of the system 100 is configured to receive a sensor data from the plurality of sensor slaves 110,112,114,116 and regulate at least one environmental parameter of the at least one hydroponic farm. The artificial intelligence module

104 of the system 100 is configured to provide at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm. The artificial intelligence module 104 comprises a predictive climate control module 406, a disease analysis module 408 and a predictive harvesting module 410.
In yet another embodiment, the system 100 comprises an on-site master controller 102 communicably coupled to a server arrangement 106, an artificial intelligence module 104 and a plurality of sensor slaves 110,112, 114, 116. The on-site master controller 102 of the system 100 is configured to receive a sensor data from the plurality of sensor slaves 110,112,114,116 and regulate at least one environmental parameter of the at least one hydroponic farm. The artificial intelligence module 104 of the system 100 is configured to provide at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm. The on-site master controller 102 comprises a slave configuration module 204, a data logging and analysis module 206, an on-site admin module 208, a data synchronisation module 210 and an on-site database 212. The server arrangement 106 comprises a farm registration module 308, a super admin control module 310, a remote monitoring and configuration module 312, and a database arrangement 314. The artificial intelligence module 104 comprises a predictive climate control module 406, a disease analysis module 408 and a predictive harvesting module 410.
Referring to Fig. 5, there is illustrated a flow chart of a method 500 for automating and controlling at least one hydroponic farm, in accordance with an embodiment of the present disclosure. At step 502, the method 500 comprises receiving sensor data from a plurality of sensor slaves. At step 504, the method 500 comprises regulating at least one environmental parameter of the at least one hydroponic farm using an on-site master controller, wherein the on-site master controller is communicably coupled to a server arrangement, an artificial intelligence module and a plurality of sensor slaves. At step 506, the method 500 comprises providing at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation

in the at least one hydroponic farm, using the artificial intelligence module. FIG. 5 is merely an example, which should not unduly limit the scope of the claims herein. A person skilled in the art will recognize many variations, alternatives and modifications of embodiments of the present disclosure.
The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the spirit or scope of the present disclosure.

We Claim:

1. A system for automation and control of at least one hydroponic farm, the
system comprising an on-site master controller communicably coupled to a server
arrangement, an artificial intelligence module and a plurality of sensor slaves, the
on-site master controller is configured to:
- receive a sensor data from the plurality of sensor slaves; and
- regulate at least one environmental parameter of the at least one hydroponic farm, characterized in that the artificial intelligence module is configured to provide at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm.

2. The system as claimed in claim 1, wherein the on-site master controller comprises a slave configuration module, a data logging and analysis module, an on-site admin module, a data synchronisation module and an on-site database.
3. The system as claimed in claim 2, wherein the slave configuration module is configured to register the plurality of sensor slaves, receive a flow of the sensor data from the plurality of sensor slaves, and communicate triggers generated by the on-site master controller for the regulation of the at least one environmental parameter in the hydroponic farm.
4. The system as claimed in claim 2, wherein the data logging and analysis module is configured to log the received sensor data in the on-site database for monitoring of the at least one hydroponic farm, and filter, segregate and process the received sensor data for data analysis.
5. The system as claimed in claim 2, wherein the on-site admin module is configured to receive input from an on-site user for the regulation of the at least one environmental parameter in the hydroponic farm.
6. The system as claimed in claim 2, wherein the data synchronisation module is configured to send the processed sensor data to the server arrangement and the artificial intelligence module, and receive communication from the server arrangement and the artificial intelligence module.

7. The system as claimed in claim 2, wherein the on-site database is configured to store the logged sensor data, the processed sensor data, the inputs received from the on-site user, the triggers generated by the on-site master controller, the communication received from the server arrangement and the communication received from the artificial intelligence module.
8. The system as claimed in claim 1, wherein the server arrangement comprises a farm registration module, a super admin control module, a remote monitoring and configuration module, and a database arrangement.
9. The system as claimed in claim 8, wherein the farm registration module is configured to register at least one hydroponic farm with the super admin control module.
10. The system as claimed in claim 8, wherein the super admin control module is configured to generate authentication details of the at least one registered hydroponic farm for the on-site admin module and the remote monitoring and configuration module.
11. The system as claimed in claim 8, wherein the super admin control module is configured to receive input from a super admin for the regulation of the at least one environmental parameter in the hydroponic farm.
12. The system as claimed in claim 8, wherein the remote monitoring and configuration module is configured to present the processed sensor data to a remote user and receive input from the remote user for the regulation of the at least one environmental parameter in the hydroponic farm.
13. The system as claimed in claim 1, wherein the server arrangement is configured to receive the processed sensor data from the data synchronisation module and communicate the inputs received from the remote user and the super admin to the on-site master controller via the data synchronisation module.
14. The system as claimed in claim 8, wherein the database arrangement is configured to store the registered hydroponic farm details, the generated

authentication details, the inputs received from the super admin, the inputs received from the remote user, and the processed sensor data received from the data synchronisation module.
15. The system as claimed in claim 1, wherein the plurality of sensor slaves includes motor controller slave, water level sensor slave, ambient temperature sensor slave, ambient humidity sensor slave, pH sensor slave, electrical conductivity sensor slave, flow sensor slave, dissolved oxygen sensor slave, Photosynthetically Active Radiation sensor slave, air flow sensor slave, CO2 sensor slave, root zone temperature sensor slave and root zone humidity sensor slave.
16. The system as claimed in claim 1, wherein the on-site master controller is configured to generate the triggers to regulate the at least one environmental parameter based on the at least one of:

- the processed sensor data generated by the processing of the received sensor data from the plurality of sensor slaves;
- the inputs from the on-site user via the on-site admin module;
- the communication received from the artificial intelligence module; and
- the communication received from the server arrangement.

17. The system as claimed in claim 16, wherein the generated triggers actuate at least one actuator from a plurality of actuators to regulate the at least one environmental parameter.
18. The system as claimed in claim 1, wherein the artificial intelligence module comprises a predictive climate control module, a disease analysis module and a predictive harvesting module.
19. The system as claimed in claim 18, wherein the predictive climate control module is configured to receive the processed sensor data to predict the at least one environmental parameter and communicate a generated instruction to the on-site master controller for the predictive climate control by the regulation of the at least one environmental parameter in the hydroponic farm.

20. The system as claimed in claim 18, wherein the disease analysis module is coupled with a plurality of hyperspectral imaging modules and configured to receive the processed sensor data and a hyperspectral imaging data to predict occurrence of a disease and communicate a generated instruction to the on-site master controller for a disease mitigating action.
21. The system as claimed in claim 18, wherein the predictive harvesting module is coupled with the plurality of hyperspectral imaging modules and configured to receive the processed sensor data and a hyperspectral imaging data to predict a harvesting timeline and a quality of harvest.
22. A method for automating and controlling at least one hydroponic farm, the method comprising:

- receiving sensor data from a plurality of sensor slaves; and
- regulating at least one environmental parameter of the at least one hydroponic farm using an on-site master controller, wherein the on-site master controller is communicably coupled to a server arrangement, an artificial intelligence module and a plurality of sensor slaves,
characterized in that the method comprises providing at least one of: a predictive climate control, a disease analysis and a predictive harvesting automation in the at least one hydroponic farm, using the artificial intelligence module.