Description:DESCRIPTION:
Field of the invention:
[0001] The present disclosure generally relates to the technical field of mart agriculture and precision farming, and in specific, relates to an IoT-based system and method for automated irrigation and fertigation is adapted for sugarcane cultivation by integrating sensor-based environmental monitoring, mobile-based remote control, and machine learning algorithms to optimize water and fertilizer use, thereby enhancing crop yield and promoting sustainable farming practices.
Background of the invention:
[0002] Sugarcane (Saccharum officinarum) is a vital commercial crop used in sugar and biofuel production, with high demands for water and nutrients during key growth stages like tillering, elongation, and maturity. Efficient irrigation and fertigation are crucial for maximizing yield and maintaining soil health. Poor timing or improper application can lead to water stress, nutrient imbalances, and environmental harm. Traditional methods—manual fertilization and basic irrigation systems—lack precision and automation, especially in major producing regions like India. With growing water scarcity, there is an urgent need to adopt smarter, more sustainable resource management practices. Enhancing irrigation and fertigation techniques can improve productivity, lower input costs, and ensure long-term viability of sugarcane cultivation in water-stressed areas.

[0003] Farmers growing sugarcane encounter persistent challenges in managing irrigation and fertilization effectively. Manual methods, which are still widely used, are labor-intensive and often lead to uneven water and nutrient distribution, particularly across large or irregularly shaped fields. As a result, some areas experience overwatering and nutrient leaching, while others face drought stress and nutrient deficiencies. Declining availability of skilled labor and rising labor costs further strain the economic viability of manual practices. In regions with unstable electricity supply, operating irrigation pumps becomes inconsistent, disrupting timely water delivery during critical growth stages. Additionally, limited access to real-time environmental data—such as soil moisture levels and weather forecasts—leads to poorly timed irrigation or fertigation. This inefficiency contributes to water wastage, increased input costs, and lower crop productivity, while requiring frequent field visits that add fuel and transport expenses.

[0004] Sugarcane farmers have historically relied on traditional irrigation methods like flood irrigation, furrow irrigation, and manual canal diversion. These techniques are inefficient in water use. For example, flood irrigation applies excessive water across the field, leading to significant runoff and evaporation. Fertilizers are often applied manually or during irrigation without considering real-time soil conditions, making it hard to adjust doses based on actual nutrient needs or soil fertility. Additionally, there is no method for tracking the volume of water applied or its penetration depth, leading to overuse and nutrient loss. While these methods are inexpensive, they are inefficient and unsuitable for sustainable agriculture, especially with increasing water scarcity and climate change challenges.

[0005] Modern irrigation and fertigation technologies like drip irrigation, pivot irrigation, and fertigation pumps aim to improve water and fertilizer efficiency. Some systems integrate soil moisture sensors, wireless communication, and remote controls. However, many of these technologies are too costly or complex for small- and medium-scale farmers, particularly in developing regions. Additionally, these systems often lack integration between sensing, decision-making, and actuation, requiring manual intervention based on intuition rather than real-time data. Many systems also lack mobile control or multi-tank fertigation, reducing flexibility. Furthermore, most do not incorporate advanced algorithms, such as machine learning, to adjust irrigation and fertigation based on soil and weather conditions. This lack of integration and intelligence limits their effectiveness and adoption, especially among resource-constrained farmers.

[0006] While several patents and research initiatives propose automated irrigation and fertilizer management systems, most focus on isolated aspects like sensor monitoring or remote control, without providing an integrated, scalable, and user-friendly solution. Systems such as those in US20160202679A1 and WO2013138879A1 include advanced features like crop sensors and light-sensitive controls but require high initial investments, technical expertise, and proprietary components. Few solutions offer real-time mobile communication or integrate predictive algorithms, like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), for adaptive control based on field data. This limits the adoption of automation and data-driven decision-making by farmers. There is a need for an affordable, easy-to-deploy, IoT-enabled system that optimizes irrigation and fertigation, compatible with organic and inorganic fertilizers and renewable energy sources.

[0007] Therefore, there is a need for an IoT-based system that significantly reduces water consumption (up to 60%) and energy usage by optimizing pump operation and flow control. There is also a need for an IoT-based system that is compatible with both organic and inorganic liquid fertilizers, offering flexibility in fertigation practices and supporting sustainable farming techniques. Furthermore, there is also a need for an IoT-based system that provides real-time feedback on soil moisture, temperature, pressure, water flow, power status, and system faults (like motor failures or dry runs).
Objectives of the invention:
[0008] The primary objective of the present invention is to provide an IoT-based system and method for automated irrigation and fertigation is adapted for sugarcane cultivation by integrating sensor-based environmental monitoring, mobile-based remote control, and machine learning algorithms to optimize water and fertilizer use, thereby enhancing crop yield and promoting sustainable farming practices.

[0009] Another objective of the present invention is to provide an IoT-based system that enables automated delivery of water and nutrients based on real-time soil and environmental data.

[0010] Another objective of the present invention is to provide an IoT-based system that ensures optimal hydration and nutrition tailored to the specific needs of sugarcane at different growth stages, leading to improved crop health and higher yields.

[0011] Another objective of the present invention is to provide an IoT-based system that predicts irrigation and fertigation schedules based on historical and live sensor inputs by leveraging recurrent neural network (RNN) and long short-term memory (LSTM) models.

[0012] Another objective of the present invention is to provide an IoT-based system that is a user-friendly mobile application that allows farmers to remotely monitor and control irrigation valves, fertigation operations, water usage, power supply, and soil conditions from anywhere, reducing the need for physical field visits.

[0013] Another objective of the present invention is to provide an IoT-based system that provides real-time feedback on soil moisture, temperature, pressure, water flow, power status, and system faults.

[0014] Another objective of the present invention is to provide an IoT-based system that is compatible with both organic and inorganic liquid fertilizers, offering flexibility in fertigation practices and supporting sustainable farming techniques.

[0015] Another objective of the present invention is to provide an IoT-based system that significantly reduces water consumption (up to 60 percent) and energy usage by optimizing pump operation and flow control.

[0016] Another objective of the present invention is to provide an IoT-based system that enables farmers to tailor irrigation and fertilization strategies to the specific needs of their sugarcane fields, thereby ensuring adaptability across diverse cultivation environments.

[0017] Yet another objective of the present invention is to provide an IoT-based system that supports connection of up to 999 field valves and multiple fertilizer injectors, allowing farmers to customize the setup according to their specific field layout, soil type, and crop requirements.

[0018] Further objective of the present invention is to provide an IoT-based system that enhances sugarcane growth and productivity, leading to a yield increase of 15 to 40 percent, while also preventing issues such as nutrient leaching and salinity, thereby preserving soil health.
Summary of the invention:
[0019] The present disclosure proposes. The following presents a simplified summary in order to provide a basic understanding of some aspects of the claimed subject matter. This summary is not an extensive overview. It is not intended to identify key/critical elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0020] In order to overcome the above deficiencies of the prior art, the present disclosure is to solve the technical problem to provide an IoT-based system and method for automated irrigation and fertigation is adapted for sugarcane cultivation by integrating sensor-based environmental monitoring, mobile-based remote control, and machine learning algorithms to optimize water and fertilizer use, thereby enhancing crop yield and promoting sustainable farming practices.

[0021] According to one aspect, the invention provides an IoT-based system for automated irrigation and fertigation in sugarcane cultivation. In one embodiment, the IoT-based system comprises a computing device having a processor and a memory for storing one or more instructions executable by the processor. The processor is configured to execute a plurality of modules for monitoring, controlling, and optimizing irrigation and fertigation operations in a sugarcane field. The computing device is in communication with an application server via a network.

[0022] In one embodiment herein, the plurality of modules comprises a sensor data acquisition module, a prediction module, a control module, a communication module, a mobile interface module, and a power management module. In one embodiment herein, the sensor data acquisition module is configured to collect environmental and soil parameters of a crop field by using plurality of sensors. The prediction module including a machine learning model implemented using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). Additionally, the prediction module is configured to predict optimal irrigation timing and fertigation dosage based on the collected data and historical patterns.

[0023] In one embodiment herein, the control module is configured to generate control signals for actuating a plurality of irrigation solenoid valves and fertilizer injectors, based on the predictions made by the prediction module. The communication module is configured to transmit and receive control signals and data between the computing device, sensor nodes, valve controllers, and the application server using long range wide area network (LoRaWAN), RS485, and communication protocols.

[0024] In one embodiment herein, the mobile interface module is configured to enable remote monitoring and control of irrigation and fertigation operations via a mobile application, and to provide real-time feedback on parameters including valve status, water flow rate, power availability, soil health, and battery level. The power management module is configured to manage electrical power to the system components.

[0025] The plurality of sensors including at least one soil moisture sensor, at least one soil temperature sensor, at least one atmospheric temperature sensor, at least one humidity sensor, and at least one water flow and a pressure sensor, each operatively coupled to the computing device and configured to continuously monitor environmental and field conditions for optimizing irrigation and fertigation operations. The computing device is configured to control a plurality of irrigation solenoid valves through a wired or wireless valve controller capable of managing up to 999 valves.

[0026] The machine learning model within the prediction module is trained on time-series datasets encompassing environmental parameters, historical water usage, and crop growth patterns to accurately forecast optimal irrigation intervals, enabling data-driven and timely decision-making. The mobile interface module is configured to provide features for remote access, control, and status alerts via SMS, IVRS, missed calls, and mobile application notifications.

[0027] The communication module utilizes the LoRaWAN gateway integrated with RS485 and RS232 protocols for data transmission over long distances in rural agricultural fields. Tin another embodiment herein, the computing device is configured to automatically detect abnormal operating conditions such as dry run of pumps, power failure, and motor faults, and generate real-time alerts to the mobile application via SMS.

[0028] In one embodiment herein, the power management module includes a solar panel rated at 6V/5W and a micro-hydroelectric generator. The power management module is configured to charge a rechargeable lithium battery for uninterrupted field operation. In one embodiment herein, the application server is configured to store historical sensor data and system’s performance metrics in a cloud database for analytics, reporting, and calibration purposes.

[0029] According to another aspect, the invention provides a method for operating an IoT-based system for automated irrigation and fertigation in sugarcane cultivation. At one step, the sensor data acquisition module collects the environmental and field data of the sugarcane field using the plurality of sensors and transmits the sensor data to the computing device via the network. At another step, the prediction module processes the receiving the sensor data using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) to predict optimal irrigation timing and fertigation dosage based on the collected data and historical patterns.

[0030] At another step, the control module generates the control signals to activate and deactivate irrigation solenoid valves and fertilizer injectors, based on the predictions made by the prediction module, thereby delivering calculated amounts of water and fertilizer to the sugarcane crop through a flow-regulated pipeline network. At another step, the mobile interface module monitors operational performance and status of the IoT-based system in real-time feedback, including soil conditions, valve status, battery level, water flow, and power supply are presented to the user, generating alerts and notifications to the user.

[0031] At another step, the application server stores the sensor readings, crop irrigation logs in a cloud database for future analysis, reporting, and calibration. At another step, the IoT-based system allows the user to manually control the IoT-based system via a mobile application using commands such as ON/OFF toggling, selecting zones, and adjusting fertigation parameters in real-time. Further, at another step, the communication module receives the control signals and data between the computing device, sensor nodes, valve controllers, and the application server using long range wide area network (LoRaWAN), RS485, and communication protocols.

[0032] Further, objects and advantages of the present invention will be apparent from a study of the following portion of the specification, the claims, and the attached drawings.
Detailed description of drawings:
[0033] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate an embodiment of the invention, and, together with the description, explain the principles of the invention.

[0034] FIG. 1 illustrates a block diagram of an IoT-based system for automated irrigation and fertigation in sugarcane cultivation, in accordance to an exemplary embodiment of the invention.

[0035] FIGs. 2A-2B illustrate screenshots of the IoT-based system that is adapted to depict user interface, in accordance to an exemplary embodiment of the invention.

[0036] FIGs. 2C-2D illustrate screenshots of the IoT-based system that is adapted to control sensors and voltage, in accordance to an exemplary embodiment of the invention.

[0037] FIGs. 2E-2F illustrate screenshots of the IoT-based system that is adapted to control water usage and valve log report, in accordance to an exemplary embodiment of the invention.

[0038] FIGs. 2G-2H illustrate screenshots of the IoT-based system that is adapted to depicts setting and status, in accordance to an exemplary embodiment of the invention.

[0039] FIG. 3 illustrates a block diagram of the IoT-based system, in accordance to an exemplary embodiment of the invention.

[0040] FIG. 4 illustrates a flowchart of a method for operating the IoT-based system for automated irrigation and fertigation in sugarcane cultivation, in accordance to an exemplary embodiment of the invention.
Detailed invention disclosure:
[0041] Various embodiments of the present invention will be described in reference to the accompanying drawings. Wherever possible, same or similar reference numerals are used in the drawings and the description to refer to the same or like parts or steps.

[0042] The present disclosure has been made with a view towards solving the problem with the prior art described above, and it is an object of the present invention to provide an IoT-based system and method for automated irrigation and fertigation is adapted for sugarcane 100 cultivation by integrating sensor-based environmental monitoring, mobile-based remote control, and machine learning algorithms to optimize water and fertilizer use, thereby enhancing crop yield and promoting sustainable farming practices.

[0043] According to one exemplary embodiment of the invention, FIG. 1 refers to a block diagram of the IoT-based system 100 for automated irrigation and fertigation in sugarcane cultivation. In one embodiment herein, the IoT-based system 100 enables automated delivery of water and nutrients based on real-time soil and environmental data. The IoT-based system 100 ensures optimal hydration and nutrition tailored to the specific needs of sugarcane at different growth stages, leading to improved crop health and higher yields. The IoT-based system 100 predicts irrigation and fertigation schedules based on historical and live sensor inputs by leveraging recurrent neural network (RNN) and long short-term memory (LSTM) models.

[0044] In another embodiment herein, the IoT-based system 100 comprises a computing device 102 having a processor 104 and a memory 106 for storing one or more instructions executable by the processor 104. The processor 104 is configured to execute a plurality of modules 108 for monitoring, controlling, and optimizing irrigation and fertigation operations in a sugarcane field. The computing device 102 is in communication with an application server 124 via a network 122.

[0045] In one embodiment herein, the plurality of modules 108 comprises a sensor data acquisition module 110, a prediction module 112, a control module 114, a communication module 116, a mobile interface module 118, and a power management module 120. In one embodiment herein, the sensor data acquisition module 110 is configured to collect environmental and soil parameters of a crop field by using plurality of sensors. The prediction module 112 including a machine learning model implemented using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). Additionally, the prediction module 112 is configured to predict optimal irrigation timing and fertigation dosage based on the collected data and historical patterns.

[0046] In one embodiment herein, the control module 114 is configured to generate control signals for actuating a plurality of irrigation solenoid valves and fertilizer injectors, based on the predictions made by the prediction module 112. The communication module 116 is configured to transmit and receive control signals and data between the computing device 102, sensor nodes, valve controllers, and the application server 124 using long range wide area network (LoRaWAN), RS485, and communication protocols.

[0047] In one embodiment herein, the mobile interface module 118 is configured to enable remote monitoring and control of irrigation and fertigation operations via a mobile application, and to provide real-time feedback on parameters including valve status, water flow rate, power availability, soil health, and battery level. The power management module 120 is configured to manage electrical power to the system components.

[0048] The plurality of sensors including at least one soil moisture sensor, at least one soil temperature sensor, at least one atmospheric temperature sensor, at least one humidity sensor, and at least one water flow and a pressure sensor, each operatively coupled to the computing device 102 and configured to continuously monitor environmental and field conditions for optimizing irrigation and fertigation operations. The computing device 102 is configured to control a plurality of irrigation solenoid valves through a wired or wireless valve controller capable of managing up to 999 valves.

[0049] The machine learning model within the prediction module 112 is trained on time-series datasets encompassing environmental parameters, historical water usage, and crop growth patterns to accurately forecast optimal irrigation intervals, enabling data-driven and timely decision-making. The mobile interface module 118 is configured to provide features for remote access, control, and status alerts via SMS, IVRS, missed calls, and mobile application notifications.

[0050] The communication module 116 utilizes the LoRaWAN gateway integrated with RS485 and RS232 protocols for data transmission over long distances in rural agricultural fields. Tin another embodiment herein, the computing device 102 is configured to automatically detect abnormal operating conditions such as dry run of pumps, power failure, and motor faults, and generate real-time alerts to the mobile application via SMS.

[0051] In one embodiment herein, the power management module 120 includes a solar panel rated at 6V/5W and a micro-hydroelectric generator. The power management module 120 is configured to charge a rechargeable lithium battery for uninterrupted field operation. In one embodiment herein, the application server 124 is configured to store historical sensor data and system’s performance metrics in a cloud database for analytics, reporting, and calibration purposes.

[0052] According to another exemplary embodiment of the invention, FIGs. 2A-2B refer to screenshots (200, 202) of the IoT-based system 100 that is adapted to depict user interface. In one embodiment herein, the IoT-based system 100 incorporates a mobile-based dashboard interface that enables users to remotely monitor and control irrigation and fertigation activities across multiple farms or zones in real time. Serving as a central control hub, this dashboard is a key feature of the IoT-enabled smart system designed specifically for sugarcane cultivation. The dashboard provides a unified view of multiple farms or fields on a single screen, enhancing operational visibility and efficiency. Each farm is represented as a separate module displaying. This multi-farm capability allows farmers and administrators to oversee and manage several geographically dispersed fields using a single mobile device, improving convenience and informed decision-making.

[0053] In one embodiment herein, the real-time irrigation status that displayed as a percentage. The real-time monitoring loop ensures users are constantly aware of system performance and can respond immediately to changing field conditions. The dashboard features a dynamic timer module that facilitates precise irrigation management by displaying. The user manually configures these parameters or rely on the automated prediction engine, which leverages RNN-LSTM algorithms to determine optimal irrigation durations based on historical and real-time data.

[0054] According to another exemplary embodiment of the invention, FIGs. 2C-2D refer to screenshots (204, 206) of the IoT-based system 100 that is adapted to control sensors and voltage. In one embodiment herein, the left screen of the image displays detailed readings from soil moisture sensors and environmental sensors installed across various zones of the farm. The soil moisture is configured to monitor real-time percentage values are shown (e.g., 36.58% for soil, 41.47% for surrounding conditions). The reading is visualized with thermometer-style indicators, providing a quick and clear status of soil saturation.

[0055] These sensors help determine whether irrigation is required and directly influence the automatic start/stop logic of the system. The dashboard includes a visual status for each sensor (green tick indicates healthy operation. Alerts can be generated if readings are not received or if thresholds exceed predefined safe zones. This monitoring helps in optimizing operational cost and detecting energy inefficiencies or equipment faults.

[0056] In one embodiment herein, the voltage and current logging system provides a detailed graph-based analysis of the voltage and current over time for a selected farm (RK Farms). The real-time graph plots voltage levels against time, enabling trend analysis and historical referencing. The blue curve represents actual voltage levels, while horizontal lines represent low and high voltage thresholds. This data helps identify patterns like voltage drops, surges, or inconsistent power supply. Additionally, the interface supports toggling between "VOLTAGE" and "CURRENT" views.

[0057] According to another exemplary embodiment of the invention, FIGs. 2E-2F refer to screenshots (210, 212) of the IoT-based system 100 that is adapted to control water usage and valve log report. In one embodiment herein, the flow report provides detailed insights into water usage based on time and date. The bar graph visualization (WATER LEVEL FLOW) displays daily water flow volumes distributed across specific time blocks or irrigation zones. The y-axis represents eater quantity (liters or kiloliters), and x-axis represents the time intervals or operational segments (e.g., early morning to evening sessions). It enables identification of high-usage periods and optimization of watering schedules. This enables precise water auditing, regulatory compliance, and sustainable water use

[0058] In another embodiment herein, the valve usage log interface (NEF Pudumathi Display – Right Screen) that shows the Valve Log Report, documenting the activation times for each valve associated with different irrigation lines or fertigation sections. The users can define custom start and end dates for generating the log, and allows historical data comparison and periodic performance reviews. The fertigation support is configured to precise valve operation is key for timed nutrient delivery via fertigation, reducing wastage and promoting uniform crop nutrition.

[0059] According to another exemplary embodiment of the invention, FIGs. 2G-2H refer to screenshots (212, 214) of the IoT-based system 100 that is adapted to depicts setting and status. In one embodiment herein, the control menu is configured to every component of the smart irrigation system. The node setting is configured to parameters for IoT node devices (sensors, controllers, etc.), and includes communication protocols, firmware updates, and node identification. The valve settings manage settings for irrigation and fertigation valves, and define timing, operation modes, delays, and feedback options. The mode settings configure switch between modes such as automatic, manual, fertigation, or scheduled irrigation. The log report access historical logs for water usage, valve operations, system alerts, and events.

[0060] In one embodiment herein, the controlled status dashboard represents the real-time status of various functional parameters and modules of the IoT-based system 100. The light indicates IoT-based system lighting status (used for internal visibility or visual diagnostics). The status of communication bus alerts (red icon indicates issues or failure to sync with nodes). The valve feedback represents the real-time feedback from valve actuators (green tick for successful action, red cross for failure or no response). The network time & network range that sync timing and signal strength of the communication network (e.g., LoRaWAN or Wi-Fi).

[0061] According to another exemplary embodiment of the invention, FIGs. 3A-3B refer to block diagrams (300, 302) of the IoT-based system 100. In one embodiment herein, the IoT-based system 100 is adapted for sugarcane cultivation. It integrates durable hardware, sensor networks, real-time monitoring, machine learning, and mobile connectivity to optimize water and fertilizer usage while improving crop yield and promoting sustainable agriculture. A key component of the IoT-based system 100 is a high-durability flow meter that measures water volume with an accuracy of ±0.5%, supporting flow rates from 0.3 to 10 m/s and pipe sizes ranging from 19 mm to 300 mm. Data is communicated via the RS232 protocol.

[0062] The IoT-based system 100 features a field processing unit, known as the SCON FP-L, which collects environmental data including water flow, pressure, temperature, and soil moisture. This data is transmitted wirelessly to the main control unit (MCU), which serves as the central hub for managing the system and interfacing with both field devices and a cloud server. The SCON FP-L supports two flow meters and two pressure sensors and is powered by a 230V AC supply. The main control unit orchestrates system-wide operations, including automatic activation of backwash valves to maintain proper pressure and the management of all irrigation room components. End devices in the field are powered by rechargeable lithium batteries, which are charged via a 5V hydroelectric micro-generator (F50).

[0063] This generator is activated by hydrostatic pressure whenever the solenoid valve is opened, providing a self-sustaining power source. A mobile-based dashboard interface is included in the system to allow users to remotely monitor and control multiple farms or zones simultaneously. Each field is presented as a separate module with real-time displays of soil moisture levels, irrigation status (using intuitive red and green icons), and toggles for manual or automatic control modes.

[0064] The dashboard also includes pressure monitoring for inlet (P1) and outlet (P2) values, shown through color-coded gauges that alert users to abnormal conditions. If pressure falls outside defined thresholds, the system can automatically shut off irrigation to protect equipment and crops. Analog sensors for temperature, humidity, and soil moisture provide continuous monitoring. These sensors convert physical parameters into analog electrical signals, which are digitized using Analog-to-Digital Converters (ADCs) embedded within the LoRa communication device. The LoRa microcontroller processes this data and transmits it to the network server using the LoRaWAN protocol. Sensor data is managed using firmware stored in Flash EPROM, which governs communication and data processing. The gateway of the system features a 16-pin configuration, with dedicated pins for power supply, RS485 communication, digital inputs, and analog voltage/current channels.

[0065] Machine learning plays a pivotal role in the system through the implementation of a Recurrent Neural Network (RNN) model enhanced with Long Short-Term Memory (LSTM) cells. The prediction module (112) is trained using time-series data from environmental sensors, historical water usage, and crop growth patterns to forecast optimal irrigation intervals. The RNN architecture consists of multiple layers, where each neuron is activated by nonlinear functions and the output of previous time steps is fed as input to the current step, allowing for intelligent temporal decision-making. Core equations define how the input, forget, and output gates manage internal memory (Ct) and state transitions (ht). The RNN is capable of storing information for future reference and is well-suited for sequence-based predictions such as irrigation scheduling and crop yield estimation. Compared to traditional machine learning algorithms like random forest, decision trees, logistic regression, SVM, and MLP, the RNN with LSTM offers superior accuracy, high throughput, and lower energy consumption.

[0066] The IoT-based system 100 uses real-time environmental data such as temperature, humidity, rainfall, and soil moisture levels to determine the most suitable crops for current and future climate conditions. RNNs have demonstrated exceptional performance in handling long sequences of data, particularly in time-series prediction tasks. The feedback path between hidden layers allows the model to learn long-term dependencies, improving the ability to predict irrigation needs and optimize fertigation processes. By automating decision-making, the system reduces manual effort, minimizes the need for frequent field visits, and increases operational efficiency. Once a farmer activates the system, the RNN analyzes power availability, soil moisture, weather conditions, and irrigation requirements before proceeding with automated actions. This results in up to 60% water savings and a yield improvement of over 40% in sugarcane cultivation.

[0067] According to another exemplary embodiment of the invention, FIG. 4 refers to a flowchart 400 of a method for operating the IoT-based system 100 for automated irrigation and fertigation in sugarcane cultivation. At step 402, the sensor data acquisition module 110 collects the environmental and field data of the sugarcane field using the plurality of sensors and transmits the sensor data to the computing device 102 via the network 12. At step 404, the prediction module 112 processes the receiving the sensor data using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) to predict optimal irrigation timing and fertigation dosage based on the collected data and historical patterns.

[0068] At step 406, the control module 114 generates the control signals to activate and deactivate irrigation solenoid valves and fertilizer injectors, based on the predictions made by the prediction module 112, thereby delivering calculated amounts of water and fertilizer to the sugarcane crop through a flow-regulated pipeline network. At step 408, the mobile interface module 118 monitors operational performance and status of the IoT-based system 100 in real-time feedback, including soil conditions, valve status, battery level, water flow, and power supply are presented to the user, generating alerts and notifications to the user.

[0069] At step 410, the application server 124 stores the sensor readings, crop irrigation logs in a cloud database for future analysis, reporting, and calibration. At step 412, the IoT-based system 100 allows the user to manually control the IoT-based system 100 via a mobile application using commands such as ON/OFF toggling, selecting zones, and adjusting fertigation parameters in real-time. At step 414, the communication module 116 receives the control signals and data between the computing device 102, sensor nodes, valve controllers, and the application server 124 using long range wide area network (LoRaWAN), RS485, and communication protocols.

[0070] Numerous advantages of the present disclosure may be apparent from the discussion above. In accordance with the present disclosure, the IoT-based system 100 is disclosed. The proposed IoT-based system 100 ensures optimal hydration and nutrition tailored to the specific needs of sugarcane at different growth stages, leading to improved crop health and higher yields. The proposed IoT-based system 100 predicts irrigation and fertigation schedules based on historical and live sensor inputs by leveraging recurrent neural network (RNN) and long short-term memory (LSTM) models. The proposed IoT-based system 100 user-friendly mobile application that allows farmers to remotely monitor and control irrigation valves, fertigation operations, water usage, power supply, and soil conditions from anywhere, reducing the need for physical field visits.

[0071] The proposed IoT-based system 100 provides real-time feedback on soil moisture, temperature, pressure, water flow, power status, and system faults. The proposed IoT-based system 100 compatible with both organic and inorganic liquid fertilizers, offering flexibility in fertigation practices and supporting sustainable farming techniques. The proposed IoT-based system 100 significantly reduces water consumption (up to 60 percent) and energy usage by optimizing pump operation and flow control. The proposed IoT-based system 100 enables farmers to tailor irrigation and fertilization strategies to the specific needs of their sugarcane fields, thereby ensuring adaptability across diverse cultivation environments.

[0072] It will readily be apparent that numerous modifications and alterations can be made to the processes described in the foregoing examples without departing from the principles underlying the invention, and all such modifications and alterations are intended to be embraced by this application. 
, Claims:CLAIMS:
I/We Claim:
1. An IoT-based system (100) for automated irrigation and fertigation in sugarcane cultivation, comprising:
a computing device (102) having a processor (104) and a memory (106) for storing one or more instructions executable by the processor (104), wherein the processor (104) is configured to execute a plurality of modules (108) for monitoring, controlling, and optimizing irrigation and fertigation operations in a sugarcane field, wherein the computing device (102) is in communication with an application server (124) via a network (122), wherein the plurality of modules (108) comprises:
a sensor data acquisition module (110) configured to collect environmental and soil parameters of a crop field by using plurality of sensors;
a prediction module (112) including a machine learning model implemented using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), configured to predict optimal irrigation timing and fertigation dosage based on the collected data and historical patterns;
a control module (114) configured to generate control signals for actuating a plurality of irrigation solenoid valves and fertilizer injectors, based on the predictions made by the prediction module (112);
a communication module (116) configured to transmit and receive control signals and data between the computing device (102), sensor nodes, valve controllers, and the application server (124) using long range wide area network (LoRaWAN), RS485, and communication protocols;
a mobile interface module (118) configured to enable remote monitoring and control of irrigation and fertigation operations via a mobile application, and to provide real-time feedback on parameters including valve status, water flow rate, power availability, soil health, and battery level; and
a power management module (120) configured to manage electrical power to the system components.
2. The IoT-based system (100) as claimed in claim 1, wherein the plurality of sensors including at least one soil moisture sensor, at least one soil temperature sensor, at least one atmospheric temperature sensor, at least one humidity sensor, and at least one water flow and a pressure sensor, each operatively coupled to the computing device (102) and configured to continuously monitor environmental and field conditions for optimizing irrigation and fertigation operations.
3. The IoT-based system (100) as claimed in claim 1, wherein the computing device (102) is configured to control a plurality of irrigation solenoid valves through a wired or wireless valve controller capable of managing up to 999 valves.
4. The IoT-based system (100) as claimed in claim 1, wherein the machine learning model within the prediction module (112) is trained on time-series datasets encompassing environmental parameters, historical water usage, and crop growth patterns to accurately forecast optimal irrigation intervals, enabling data-driven and timely decision-making.
5. The IoT-based system (100) as claimed in claim 1, wherein the mobile interface module (118) provides features for remote access, control, and status alerts via SMS, IVRS, missed calls, and mobile application notifications.
6. The IoT-based system (100) as claimed in claim 1, wherein the communication module (116) utilizes the LoRaWAN gateway integrated with RS485 and RS232 protocols for data transmission over long distances in rural agricultural fields.
7. The IoT-based system (100) as claimed in claim 1, wherein the computing device (102) is configured to automatically detect abnormal operating conditions such as dry run of pumps, power failure, and motor faults, and generate real-time alerts to the mobile application via SMS.
8. The IoT-based system (100) as claimed in claim 1, wherein the power management module (120) includes a solar panel rated at 6V/5W and a micro-hydroelectric generator configured to charge a rechargeable lithium battery for uninterrupted field operation.
9. The IoT-based system (100) as claimed in claim 1, wherein the application server (124) is configured to store historical sensor data and system’s performance metrics in a cloud database for analytics, reporting, and calibration purposes.
10. A method for operating an IoT-based system (100) for automated irrigation and fertigation in sugarcane cultivation, comprising:
collecting, by sensor data acquisition module (110), environmental and field data of a sugarcane field using plurality of sensors and transmitting the sensor data to a computing device (102) via a network (12);
processing, by a prediction module (112), the receiving the sensor data using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) to predict optimal irrigation timing and fertigation dosage based on the collected data and historical patterns;
generating, by a control module (114), control signals to activate and deactivate irrigation solenoid valves and fertilizer injectors, based on the predictions made by the prediction module (112), thereby delivering calculated amounts of water and fertilizer to the sugarcane crop through a flow-regulated pipeline network;
monitoring, by a mobile interface module (118), operational performance and status of the IoT-based system (100) in real-time feedback, including soil conditions, valve status, battery level, water flow, and power supply are presented to the user, generating alerts and notifications to the user;
storing, by an application server (124), sensor readings, crop irrigation logs in a cloud database for future analysis, reporting, and calibration;
allowing, by the IoT-based system (100), the user to manually control the IoT-based system (100) via a mobile application using commands such as ON/OFF toggling, selecting zones, and adjusting fertigation parameters in real-time; and
receiving, by a communication module (116), control signals and data between the computing device (102), sensor nodes, valve controllers, and the application server (124) using long range wide area network (LoRaWAN), RS485, and communication protocols.