A SYSTEM AND METHOD FOR AUTOMATED DRIP IRRIGATION

Abstract

The present invention relates to a system (120) and method (300) for automated drip irrigation. The method includes receiving sensor data associated with the agricultural field (130) via the plurality of sensors (108). The method (300) further comprising analyzing the sensor data using the trained AI module to determine a required amount of water supply to the agricultural field (130), wherein the required amount of water is determined based on historical data of the agricultural field, weather patterns, and crop types. Further, the method (300) includes controlling the plurality drip irrigation nozzles (106) using the flow rate regulator (112) based on the determined required amount of water supply and the sensor data, wherein the plurality of drip irrigation nozzles is triggered using the flow rate regulator (112) when a soil moisture of the agricultural field (130) is below the threshold. Figure 3.

Specification

Description:FIELD OF THE INVENTION
[002] The present invention relates to a system and method for solar operated automated drip irrigation. More specifically, the invention relates to a system and method for an Internet of Things (IOT)-based drip irrigation solution.

BACKGROUND
[003] The demand for food production has significantly increased due to the rapid growth of the global population, especially in countries like India, where agriculture is a vital source of income for many people. Irrigation plays a transformative role in the lives of farmers by providing them with a reliable and consistent water supply, which is essential for cultivating crops and ensuring that their livelihoods are sustainable. For many farmers, particularly those in low rainfall zones, the availability of irrigation water directly influences their ability to grow food and generate income. The unpredictable rainfall patterns may lead to crop failures and financial difficulties. Irrigation enables farmers to diversify their agricultural practices by allowing them to experiment with different types of crops that may require varying amounts of water, thus reducing their dependence on a single crop. This diversification not only helps to stabilize their income but also contributes to improved food security within their communities, as a wider variety of crops can meet local dietary needs.
[004] Effective irrigation practices can improve soil health by maintaining optimal moisture levels and reducing the risk of erosion, ultimately leading to more adaptable farming methods. This adaptability is particularly important in the face of climate change, as farmers can better adapt to shifting weather patterns and prolonged periods of drought. Irrigation extends the growing season, allowing farmers to cultivate multiple harvests each year rather than being limited to a single season dictated by natural rainfall. This increased productivity translates to more significant economic opportunities, enabling farmers to invest in their operations, improve their living conditions, and support their families more effectively.
[005] Many traditional irrigation methods are inefficient because they often waste a lot of water; for example, water can evaporate into the air before it even reaches the plants, some can run off into areas where it isn't needed, and others allow water to seep too deep into the ground, where plant roots cannot access it, which results in using more water than necessary and puts unnecessary pressure on this vital resource.
[006] Smart soil moisture sensors track soil moisture levels in real time, helping farmers automate their irrigation schedules according to the exact moisture needs of their crops, but they can be expensive, which may make them difficult for small-scale farmers to afford, and they also need to be calibrated regularly to provide accurate readings, a process that can take a lot of time.
[007] Weather-based irrigation controllers utilize local weather data to automatically adjust irrigation schedules, helping to prevent overwatering during rainy periods and ensuring that water use is optimized; however, these systems can be problematic because their reliance on weather forecasts may result in inaccuracies if the forecasts are not reliable, and this over-dependence on technology might cause farmers to overlook necessary manual checks, which can ultimately lead to mismanagement of their irrigation practices.
[008] Farmers can utilize mobile apps or web platforms to monitor and control their irrigation systems from virtually any location, receiving alerts and notifications about the system's performance through remote monitoring systems; however, this convenience can be hindered in areas with poor internet connectivity, which limits the effectiveness and accessibility of these remote controls, and it also necessitates a certain level of digital literacy that may pose a barrier for some farmers who are not comfortable with technology.
[009] There are systems that integrate pest monitoring with irrigation control, aiming to optimize both water usage and crop health; however, the combination of multiple systems can introduce increased complexity and costs, and farmers may require additional training to effectively navigate and utilize these integrated systems.
[010] Automated valves and controllers in IoT systems offer the advantage of delivering water precisely when and where it's needed based on sensor data; however, they also come with several limitations, including potential mechanical failures that could disrupt irrigation, reliance on consistent power supply which may be lacking in remote areas, the need for regular maintenance to prevent issues such as clogging or wear, and the complexity of installation, which might require technical expertise that not all farmers possess. Additionally, if the sensor data is inaccurate due to environmental factors or malfunctions, it can lead to improper watering, affecting crop health.
[011] While cloud computing offers valuable capabilities for storing and analyzing data from various sensors, enabling farmers to access historical information, analyze trends, and make informed decisions about irrigation practices, it also presents several limitations, such as dependence on a reliable internet connection, which can be problematic in rural areas with poor connectivity; concerns about data security and privacy, as sensitive agricultural information may be vulnerable to breaches; potential high costs associated with data storage and processing services; and the need for digital literacy, as some farmers may struggle to navigate cloud platforms effectively without proper training and support.
[012] While community networks that facilitate data sharing among farmers can significantly enhance decision-making by allowing the exchange of insights about local conditions and best practices, they also face several limitations, such as varying levels of digital literacy among farmers, which can affect participation and the effective use of shared data; potential difficulties in establishing trust among community members regarding the accuracy and reliability of the information shared; the challenge of ensuring equitable access to technology and internet connectivity, which can create disparities in participation; and the risk of data overload, where too much information can lead to confusion rather than informed decision-making if not properly managed and curated.
[013] While open-source platforms provide customization and flexibility for building tailored irrigation solutions, they come with several limitations, including the need for technical expertise to implement and modify the systems effectively, which may be a barrier for some farmers; the potential lack of dedicated support or documentation, making troubleshooting and maintenance challenging; variability in the quality and reliability of open-source contributions, which can lead to inconsistencies in performance; and the responsibility placed on farmers to stay updated with software changes and security patches, which can be time-consuming and require ongoing commitment.
[014] Therefore, there exists a need for a system and method for providing comprehensively upgraded Internet of things (IOT) based drip irrigation system and solves the above-mentioned problems.

OBJECT OF THE INVENTION:
[015] An objective of the present invention is to improve upon the conventional problems as described above, and to provide Internet of things (IOT) based drip irrigation system.
[016] Another object of the invention is to provide a system that utilizes solar panels as a sustainable energy source, reducing reliance on conventional energy sources, lowering operational costs and minimizes carbon footprint, thereby promoting eco-friendly agricultural practices.
[017] Another object of the invention is to provide a system that delivers water directly to the roots of plants, optimizing water usage and significantly reducing waste. Programmable scheduling allows users to set irrigation times based on crop requirements, weather conditions, or soil moisture levels.
[018] Another object of the invention is to provide a system that enables remote monitoring, Internet of things (IoT) connectivity and control of the irrigation system via smartphone, tablet, or computer and provides real-time data access on irrigation status, soil conditions, and overall system performance, along with alerts for malfunctions or low water levels.
[019] Another object of the invention is to provide 7-in-1 soil integrated sensor that measures multiple soil parameters, including soil moisture, temperature, pH level, electrical conductivity, light intensity, humidity, and soil nutrients to ensure optimal irrigation and crop health.
[020] Another object of the invention is to provide a system that analyses data from the sensors to provide actionable insights and optimize irrigation practices and tracks historical data to identify trends for informed decision-making regarding crop management.
[021] Another object of the invention is to provide a system that integrates with weather forecasts to adjust irrigation schedules according to predicted rainfall and temperature changes.
[022] Another object of the invention is to provide a system that employs machine learning algorithms to adapt watering patterns based on real-time data and historical trends.
[023] Another object of the invention is to provide a system that features an intuitive dashboard for easy access to system settings, data visualization, and control options.
[024] Another object of the invention is to provide a system that offers customization options for irrigation schedules, sensor thresholds, and notification preferences.
[025] Another object of the invention is to provide a system that facilitates remote diagnostics and troubleshooting, reducing the need for on-site inspections.
[026] Another object of the invention is to provide a system that as capabilities to detect and report system issues, helping to prevent potential failures and ensure smooth operation.
[027] Another object of the invention is to provide a system that as a modular design which allows for easy expansion and integration with additional sensors or irrigation components and is adaptable to various types of crops and soil conditions, providing flexibility in usage.
[028] Another object of the invention is to provide a system that efficiently reduces water wastage by delivering the exact amount of water needed directly to plant roots and continuously monitors soil conditions to make necessary adjustments, avoiding overwatering.

SUMMARY OF THE INVENTION
[029] The present invention provides a system for automated drip irrigation. The system comprises a solar power source; a plurality of sensors, a memory includes a trained Artificial intelligence (AI) module, a plurality of drip irrigation nozzles, a user interface, a flow rate regulator, at least one processor coupled with the plurality of sensors, the solar power source, the memory, a plurality of drip irrigation nozzles, and the user interface.
[030] The at least one processor is configured to receive sensor data associated with the agricultural field via the plurality of sensors. The at least one processor is further configured analyze the sensor data using the trained AI module to determine a required amount of water supply to the agricultural field. The required amount of water is determined based on historical data of the agricultural field, weather patterns, and crop types. The at least one processor is further configured control the plurality drip irrigation nozzles using the flow rate regulator based on the determined required amount of water supply and the sensor data, wherein the plurality of drip irrigation nozzles is triggered using the flow rate regulator when a soil moisture of the agricultural field is below the threshold.
[031] In an embodiment, the present invention provides a method for automated drip irrigation. The method includes receiving sensor data associated with the agricultural field via the plurality of sensors. The method further comprising analyzing the sensor data using the trained AI module to determine a required amount of water supply to the agricultural field, wherein the required amount of water is determined based on historical data of the agricultural field, weather patterns, and crop types. Further, the method includes controlling the plurality drip irrigation nozzles using the flow rate regulator based on the determined required amount of water supply and the sensor data, wherein the plurality of drip irrigation nozzles is triggered using the flow rate regulator when a soil moisture of the agricultural field is below the threshold.

BRIEF DESCRIPTION OF THE DRAWINGS
[032] The present invention will hereinafter be described in conjunction with the accompanying drawings, wherein like numerals denote like elements. Additional embodiments of the invention will become evident upon reviewing the non-limiting embodiments described in the specification in conjunction with the accompanying drawings, wherein:
[033] Figure 1A and 1B illustrate network environment for automated drip irrigation, in accordance with an embodiment of the present invention.
[034] Figures 2 illustrates a system for automated drip irrigation, in accordance with an embodiment of the present invention.
[035] Figure 3 illustrates a method for automated drip irrigation, in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[036] Before the present configuration of a system and a method for solar operated automated drip irrigation, it is to be understood that this disclosure is not limited to particular assembly or configuration or arrangement for achieving as described, since it may vary within the specification indicated. It is further to be understood that the terminology used in the description is only for the purpose of describing the particular versions or embodiments and is not intended to limit the scope of the present invention.
[037] The words, "comprising", "having", "including" & "containing" and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items.
[038] The increasing demand for efficient water management in agriculture necessitates innovative solutions to optimize irrigation practices. Traditional irrigation systems often lead to water wastage and inadequate crop hydration, resulting in reduced agricultural productivity. In response, the proposed invention discloses the performance evaluation of a solar powered IoT-driven drip irrigation system integrated with mobile application control. This system aims to enhance water efficiency by leveraging real-time data from soil moisture, temperature, and nutrient sensors to automate irrigation processes. The challenge lies in assessing the effectiveness of this technology in various environmental conditions, measuring its impact on water conservation, crop yield, and operational efficiency. By systematically evaluating the system's performance, the research invention to provide the development of sustainable agricultural practices that maximize resource utilization while minimizing environmental impact.
[039] The proposed invention is a comprehensive technology-driven solution tailored solar operated automated drip irrigation. The present invention provides The IoT-based smart drip irrigation system that integrates advanced technologies to optimize water usage in agriculture, ensuring precision irrigation and enhancing crop yield. The Smart Village concept seeks to combine IoT with solar powered smart agriculture to address soil fertility and climate change. This technology gives farmers precise and sustainable farming techniques, which enhances agribusiness. With the use of small-scale controllers and remote sensor frameworks, IoT technology enables farmers to maintain a connection with their dwellings.
[040] Figure 1A and 1B illustrate network environment for automated drip irrigation, in accordance with an embodiment of the present invention. Referring to drawings, the network environment (100) includes an electronic device or a User Equipment (UE) (101). The electronic device (101) includes a user interface (104) and an application (102). The electronic device (101) may include the system (120) which provides automated drip irrigation. By way of example, the UE (101) is any type of mobile terminal, fixed terminal, or portable terminal including smart glasses, Augmented Reality (AR) Headsets, Virtual reality (VR) Headsets, a mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal navigation device, personal digital assistants (PDAs), audio/video player, digital camera/camcorder, positioning device, fitness device, television receiver, radio broadcast receiver, electronic book device, game device, devices associated with one or more vehicles or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof. It is also contemplated that the UE (101) can support any type of interface to the user (such as "wearable" circuitry, etc.).
[041] By way of example, the application (102) may be any type of application that is executable at the UE (101), such as mapping application, location-based service applications, navigation applications, content provisioning services, camera/imaging application, media player applications, social networking applications, calendar applications, and the like. In one embodiment, one of the applications (102) at the UE (101) may be system (120) for automated drip irrigation.
[042] In some example embodiments, the system (120) may include an Artificial Intelligence (AI) enabled data processing and decision-making system. Artificial Intelligence (AI) enabled data processing and decision-making system further includes convolution neural networks (CNNs) and recurrent neural networks (RNNs).
[043] The network environment 100 may further includes sensors 108 and. The sensor may include a 7-in-1 sensor. The 7-in-1 sensor may include a soil moisture sensor, a soil temperature sensor, a pH level sensor, a Electrical Conductivity (EC) sensor, a light intensity sensor, a humidity sensor, and a ambient temperature sensor. The soil moisture sensor measures the volumetric water content in the soil, helping to determine irrigation needs. The soil temperature sensor monitors the temperature of the soil, which can influence seed germination and plant growth. The pH level sensors measure the acidity or alkalinity of the soil, affecting nutrient availability to plants. The Electrical Conductivity (EC) assesses the salinity of the soil, which can impact plant health and growth. The light Intensity sensor measures the amount of light available to plants, influencing photosynthesis. The humidity sensor monitors the moisture level in the air, which can affect transpiration rates in plants. The ambient temperature sensor measures the temperature of the air surrounding the plants. The sensors 108 may include a a weed sensor, a leaf temperature sensor, a Vapor pressure deficit (VPD) sensor, a substrate moisture sensor, a substrate temperature sensor, velocity sensor, and sensors to track nutrients including macro and micro elements. Further, these sensors may be duplicated or multiplexed. If there are multiple sensors for the same purpose. Alternatively, the plurality of sensors may be used at the same time, and if the measurement results do not match, it may be considered that a failure has occurred.
[044] To effectively utilize a 7-in-1 sensor, it can be installed in various locations based on the specific parameters being monitored. Here are some common installation locations.
• Root Zone of Plants: The soil moisture and temperature sensors 108 may be placed at root depth (typically 6-12 inches) to accurately gauge conditions that affect plant health.
• Surface Level: The ambient temperature and humidity sensors 108 may be installed just above the ground surface to measure air temperature and humidity levels.
• Greenhouses: The sensors may be placed throughout the greenhouse to monitor temperature, humidity, and light intensity, allowing for optimal growing conditions.
• Open Fields: The sensors can be distributed across different areas of a field to assess variability in soil moisture, pH, and EC, which can differ based on soil type or crop type.
• Plant Canopies: The light intensity sensors can be installed within or above plant canopies to measure the light available for photosynthesis.
• Drip Irrigation Lines: Placing soil moisture sensors near drip irrigation lines can help determine the effectiveness of water application and optimize irrigation schedules.
• Weather Stations: Integrating with local weather stations can enhance data accuracy by providing additional context, especially for humidity and temperature readings.
[045] In an embodiment, the network environment may include a solar power source that includes solar panels (e.g., 100W). The solar panels convert the solar energy into electrical energy to power the system 120.
[046] The network environment further includes a plurality of drip irrigation nozzles (106) and a flow rate regulator. The plurality of drip irrigation nozzles (106) delivers water directly to the plant roots, minimizing water wastage. The plurality of drip irrigation nozzles may be controller by the flow rate regulator (112). The flow rate regulator (112) adjusts the water flow to individual plants based on their specific needs.
[047] In an embodiment, the network environment also includes a water tank that provides a supply of water to the drip irrigation system when needed. The water tank is connected to a water pump that pumps water from the reservoir to the irrigation system based on sensor data.
[048] In one embodiment, the communication network (116) includes one or more networks such as a data network, a wireless network, a telephony network, or any combination thereof. It is contemplated that the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UNITS), etc., as well as any other suitable wireless medium, e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof.
[049] In an embodiment, the network environment (120) may include a cloud-based storage (110). In an embodiment, the all the collected is stored in the cloud-based storage (110). In an embodiment, the cloud-based storage (110) stores historical data for analysis and to improve irrigation efficiency.
[050] In an embodiment, the user interface (230) displays real-time data, allows for manual overrides, and schedules events.
[051] Figure 2 illustrates a system for monitoring and management of an agricultural field (130), in accordance with an embodiment of the present invention. The system includes a processor (210), a memory (220), and a network interface (230). The processor (210) may also be accompanied with one or more specialized components to perform certain processing functions and tasks such as one or more digital signal processors (DSP), or one or more application-specific integrated circuits (ASIC). A DSP typically is configured to process real-world signals (e.g., sound) in real time independently of the processor. Similarly, an ASIC can be configured to performed specialized functions not easily performed by a general purposed processor. Other specialized components to aid in performing the inventive functions described herein include one or more field programmable gate arrays (FPGA) (not shown), one or more controllers (not shown), or one or more other special-purpose computer chips. The processor (210) and accompanying components have connectivity to the memory (220) via the bus. In an embodiment, the processor (210) may be an ESP32 microcontroller. The ESP32 microcontroller is a powerful, low-cost microcontroller with integrated Wi-Fi and Bluetooth capabilities, making it ideal for Internet of Things (IoT) applications. The ESP32 can be programmed using various platforms, including the Arduino IDE, Espressif's ESP-IDF (IoT Development Framework), and MicroPython. Data IOT projects logging, environmental monitoring, and remote-control applications.
[052] The memory (220) includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to recommending video content. The memory (220) also stores the data associated with or generated by the execution of the inventive steps.
[053] Network interface (230) provides a one-way or two-way communication coupling to a variety of external devices that operate with their own processors, such as printers, scanners and external disks. In general, the coupling is with a network link that is connected to a local network to which a variety of external devices with their own processors are connected. For example, network interface may be a parallel port or a serial port or a universal serial bus (USB) port on a personal computer. In some embodiments, network interface is an integrated services digital network (ISDN) card or a digital subscriber line (DSL) card or a telephone modem that provides an information communication connection to a corresponding type of telephone line. In some embodiments, a network interface is a cable modem that converts signals on bus into signals for a communication connection over a coaxial cable or into optical signals for a communication connection over a fiber optic cable.
[054] Figure 3 illustrates a method for automated drip irrigation. The method includes at step 401, the at least one processor is configured to receive sensor data associated with the agricultural field (130) via the plurality of sensors (108). The 7-in-1 soil sensor (108) collects data on soil moisture, temperature, pH, electrical conductivity, light intensity, humidity, and ambient temperature. The collected data is transmitted in real time to a central hub or cloud platform 110 for processing and analysis. In an embodiment, the plurality of sensors (108) may include a wireless network for aggregation of the sensor data. The sensor data may be transmitted to and stored in a cloud data base 110.
[055] In an embodiment, each agriculture field (130) is equipped with a suite of sensors for various applications such as measuring temperature and humidity to monitor the health of the soil and plants. In an embodiment, a wireless sensor network architecture will be established to connect all the sensors and other electronic equipment for continuous monitoring and live data collection. Several wireless transmit/receiver devices will be used to transmit and receive data to and from various other modules. A processing unit will aggregate the data and transmit it to the cloud. Further, each data point collected from a location will include its geographical information, provided by GPS/GSM modules. In an embodiment, the sensor data may be received from weather monitoring stations or external advisory bodies. The data acquisition system collects data from various sources and sends it to a cloud and AI-enabled system for processing and analysis.
[056] In an embodiment, the at least one processor (210) is configured to preprocess the collected sensor data. The preprocessing includes handling missing values, removing duplicates, and transforming the sensor data into a suitable format for analysis. In an embodiment, the plurality of sensors may include a humidity sensor, a camera, a temperature sensor, a weed sensor, a leaf temperature sensor, a Vapor pressure deficit (VPD) sensor, a substrate moisture sensor, a substrate temperature sensor, a pH-Value sensor, a velocity sensor, and sensors to track nutrients including macro and micro elements.
[057] In an embodiment, adaptive algorithms collect data from various weather sources (e.g., local meteorological stations, satellite data, or online weather services) to gather information such as temperature, humidity, precipitation predictions, wind speed, and solar radiation. An examplery collected data is shown in the table below:

SN Particulates Field value Sensors Value
1. Nitrogen 182.52 kg/ha 179.32 kg/ha
2. Phosphorous 21.45 kg/ha 23.65 kg/ha
3. Potassium 206.52 kg/ha 216.85 kg/ha
4. pH 6.8 6.7
5. Temperature 45.70C 45.30C
6. Soil Moisture 21% 26%
7. Humidity 23% 25%

[058] Referring back to Figure. 3, the method (300) includes at step 303, the at least one processor (120) is configured to analyze the sensor data using the trained AI module to determine a required amount of water supply to the agricultural field (130). The required amount of water is determined based on historical data of the agricultural field, weather patterns, and crop types.
[059] In an embodiment, the at least one processor (210) is configured to train an AI module using training data associated with a plurality of agricultural fields and store the trained AI module in the memory (220), wherein the trained AI module using generates weather forecasts.
[060] In an embodiment, the at least one processor (210) analyze historical weather patterns and their correlations with soil moisture levels and crop requirements. This helps in understanding how different weather conditions have affected irrigation needs in the past. As new weather data comes in, the processor (210) continuously updates its models to improve predictions. This includes recognizing trends and anomalies, adapting to changes in climate patterns.
[061] Using the historical data and real-time information, the processor (210) generates weather forecasts. It predicts future weather conditions that could impact soil moisture and crop health.
[062] In an embodiment, the method (300) further includes at step 305, the processor (210) configured to control the plurality drip irrigation nozzles (106) using the flow rate regulator (112) based on the determined required amount of water supply and the sensor data. The plurality of drip irrigation nozzles is triggered using the flow rate regulator (112) when a soil moisture of the agricultural field (130) is below the threshold. Based on these forecasts, the processor (210) evaluates when to irrigate, how much water to apply, and whether to adjust the irrigation schedule to account for expected rainfall or temperature changes. If the forecast indicates rain, the processor (210) can delay or reduce irrigation, preventing overwatering. Conversely, if dry conditions are expected, it can increase the irrigation schedule to ensure plants receive adequate moisture. The system (120) can notify farmers through mobile apps or dashboards, providing insights into upcoming weather changes and suggesting irrigation adjustments
[063] In an embodiment, the processor defines optimal thresholds for each parameter based on crop requirements and environmental conditions (e.g., ideal soil moisture levels for specific crops). The processor (210) analyzes real-time data against these thresholds to determine the current irrigation needs.
[064] In an embodiment, the processor (210) triggers the plurality of drip irrigators, if soil moisture is below the threshold. In an embodiment, if soil temperature or other conditions indicate potential stress, adjust the irrigation schedule accordingly. In an embodiment, the processor (210) considers upcoming weather forecasts (e.g., expected rainfall) to avoid over-irrigation. In an embodiment, the processor (210) modifies the irrigation schedule dynamically based on real- time data. For example, the increase irrigation frequency during dry spells or reduce or suspend irrigation before expected rain. After irrigation events, the processor (210) monitors the actual weather and soil conditions. This feedback is used to refine its predictive models, ensuring that the system becomes increasingly accurate over time.
[065] In an embodiment, the processor (210) controls the automated valve. The processor (210) sends commands to automated valves in the drip irrigation system to start or stop water flow based on the analysis.
[066] In an embodiment, the processor (210) notifies users via a mobile app or dashboard of the changes made to irrigation schedules, ensuring transparency and control. The processor (210) ensures the solar-operated system is adequately powered. Check battery levels and solar panel functionality. The processor (210) performs a self-check of the irrigation system components (pumps, valves, sensors) to ensure they are functioning correctly.
[067] The processor (210) adjusts water pressure if necessary to ensure even distribution. The processor may continuously monitor sensor data during irrigation to assess effectiveness and adjust as needed and set up alerts for any anomalies (e.g., excessive moisture, equipment failure). In an embodiment, the processor (120) records the amount of water delivered and the duration of irrigation for future reference and analysis. After irrigation, evaluate soil moisture levels and other parameters to ensure the system met the crop's needs. The system provides farmers with access to a user-friendly dashboard to view real-time data, control settings, and receive updates.
[068] In an embodiment, the system allows manual control for users to override automated settings if needed, providing flexibility in unexpected conditions
[069] This the system disclosed herein offers a smart solution for optimizing water use in semi-arid farming. By integrating a 7-in-1 soil sensor, it effectively monitors crucial parameters like soil moisture, temperature, electrical conductivity, pH, and solar radiation. The incorporation of adaptive algorithms for weather forecasting allows for real-time adjustments to water supply, ensuring efficient irrigation based on current environmental conditions.
[070] Technical advantages of the invention:
[071] Experimental trials on a small-scale farm highlighted the system's impressive performance, achieving a 30% reduction in water usage compared to traditional methods while keeping plant health at optimal levels. This showcases the potential of IoT-driven technologies to enhance agricultural productivity and sustainability, ultimately reducing resource wastage. Such innovations could play a vital role in addressing water scarcity issues and improving farming practices in challenging climates.

• Optimized Water Use: By accurately predicting weather patterns, adaptive algorithms help minimize water wastage and ensure that crops receive the right amount of moisture.
• Enhanced Crop Health: Timely adjustments to irrigation schedules based on forecasts lead to healthier plants and potentially higher yields.
• Resource Efficiency: The integration of solar energy with adaptive algorithms allows for sustainable irrigation practices that reduce operational costs and environmental impact.
• Real-Time Responsiveness: Farmers can respond more effectively to changing weather conditions, improving their overall management strategies.
• Comprehensive Data Collection: By measuring multiple parameters, these sensors provide a holistic view of environmental conditions, facilitating better decision-making.
• Cost-Effectiveness: Instead of installing multiple single-function sensors, a 7-in-1 sensor reduces equipment costs and installation complexity.
• Space Efficiency: These sensors take up less physical space, which is particularly beneficial in smaller fields or greenhouses.
• Data Correlation: Collecting various types of data simultaneously allows for better analysis of how different environmental factors interact, leading to improved agricultural practices

[072] Although the subject matter has been described in language specific to structural features and/or methods in considerable detail with reference to certain preferred embodiments thereof, it is to be understood that the implementations and/or embodiments are not necessarily limited to the specific features or methods described. The examples described in detail here are only some possible embodiments of the invention among others and it could be subjected to many alterations and variants within the grasp of those skilled in the art. As such, the spirit and scope of the appended claims should not be limited to the description of the preferred embodiments contained therein. , Claims:WE CLAIM:

1. A system (120) for automated drip irrigation, the system (120) comprising:
a solar power source;
a plurality of sensors (108);
a memory (220) includes a trained Artificial intelligence (AI) module;
a plurality of drip irrigation nozzles (106);
a user interface (230)
a flow rate regulator (112);
at least one processor (210) coupled with the plurality of sensors (108), the solar power source, the memory (220), a plurality of drip irrigation nozzles (106), and the user interface (230), wherein the at least one processor (210) is configured to:
receive sensor data associated with the agricultural field (130) via the plurality of sensors (108);
analyze the sensor data using the trained AI module to determine a required amount of water supply to the agricultural field (130), wherein the required amount of water is determined based on historical data of the agricultural field, weather patterns, and crop types; and
control the plurality drip irrigation nozzles (106) using the flow rate regulator (112) based on the determined required amount of water supply and the sensor data, wherein the plurality of drip irrigation nozzles is triggered using the flow rate regulator (112) when a soil moisture of the agricultural field (130) is below the threshold.

2. The system (120) as claimed in claim 1, wherein the solar power source is configured to convert solar energy into electrical energy to the system.

3. The system (120) as claimed in claim 1, wherein the sensor data includes soil moisture of the soil, a temperature associated with the agricultural field, potential of hydrogen (pH) associated with the agricultural field, Emulsifiable Concentrate (EC) associated with the agricultural field, and light levels associated with the agricultural field.

4. The system (120) as claimed in claim 1, wherein the at least one processor (210) is configured to preprocess the collected sensor data, wherein the preprocessing includes handling missing values, removing duplicates, and transforming the sensor data into a suitable format for analysis.

5. The system (120) as claimed in claim 1, wherein the at least one processor (210) is configured to:
train an AI module using training data associated with a plurality of agricultural fields; and
store the trained AI module in the memory (220), wherein the trained AI module using generates weather forecasts.

6. The system (120) as claimed in claim 1, comprising a water tank and a solar powered water pump, wherein the at least one processor is configured to control the water tank to supply water using the solar powered water pump, wherein the water supply is based on the sensor data.

7. The system (120) as claimed in claim 1, wherein the user interface is configured to display real time data associated with the agricultural field.

8. The system (120) as claimed in claim 1, wherein the sensor data includes location data of the plurality of sensors and location data of crops in the agricultural field (130).

9. The system (120) as claimed in claim 1, wherein the at least one processor (210) is configured to:
suggest one or more irrigation schedules at specific locations of the agricultural field (130), wherein the suggestion of the one or more irrigation schedules is based on the soil temperature that indicate potential stress; and
control the plurality drip irrigation nozzles (106) using a flow rate regulator based on the one or more irrigation schedules.

10. A method (300) for automated drip irrigation, the method comprising steps of:
receiving sensor data associated with the agricultural field (130) via the plurality of sensors (108);
analyzing the sensor data using the trained AI module to determine a required amount of water supply to the agricultural field (130), wherein the required amount of water is determined based on historical data of the agricultural field, weather patterns, and crop types; and
controlling the plurality drip irrigation nozzles (106) using the flow rate regulator (112) based on the determined required amount of water supply and the sensor data, wherein the plurality of drip irrigation nozzles is triggered using the flow rate regulator (112) when a soil moisture of the agricultural field (130) is below the threshold.

Documents