Description:
BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to an irrigation system and particularly to a solar-powered autonomous drip irrigation system.
Description of Related Art
[002] Efficient management of water resources in agriculture remains a critical challenge across the world. Conventional irrigation practices often consume excessive amounts of water, leading to wastage and depletion of freshwater reserves. Farmers in many regions continue to depend on irregular rainfall patterns and electricity-driven pumping systems, that causes uncertainty in crop yield and creates dependency on external energy sources. With rising demand for food production, need for sustainable irrigation solutions becomes more urgent.
[003] Drip irrigation emerged as a solution to reduce water consumption and direct water supply to plant roots. Drip irrigation improves efficiency compared to traditional flooding or sprinkler methods. However, conventional drip irrigation still relies on manual adjustments or pre-set schedules that do not fully consider variations in soil moisture, temperature, and weather conditions. As a result, either over-irrigation or under-irrigation occurs, that negatively affects both crop health and overall productivity.
[004] Existing smart irrigation systems attempt to introduce automation and precision in water usage. Some employ soil moisture sensors, flow meters, and basic weather integration to improve control. Others integrate solar panels to reduce reliance on grid electricity. Nevertheless, these systems often lack advanced adaptability, efficient energy storage, and scalable solutions for large farms. The absence of real-time decision-making capabilities and dynamic control leads to inefficiencies, while limited energy backup reduces reliability under cloudy conditions or during nighttime.
[005] There is thus a need for an improved and advanced solar-powered autonomous drip irrigation system that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a solar-powered autonomous drip irrigation system. The drip irrigation system comprising a photovoltaic panel adapted to generate electrical energy. The drip irrigation system further comprising a battery storage unit adapted to store excess energy received from the photovoltaic panel. The drip irrigation system further comprising a charge controller adapted to regulate charging and discharging of the battery storage unit. The drip irrigation system further comprising a plurality of sensors adapted to collect environmental data. The drip irrigation system further comprising a water distribution subsystem adapted to deliver water through one or more pumps, valves, and drip lines. The water distribution subsystem is adapted to utilize the electrical energy generated by the photovoltaic panel. The drip irrigation system further comprising a controller communicatively connected to the plurality of sensors and the water distribution subsystem. The controller is configured to receive the collected environmental data from the plurality of sensors; process the environmental data and weather forecast data using an artificial intelligence (AI) model; and actuate the water distribution subsystem to distribute water through a drip irrigation network via the one or more pumps and valves based on the processed environmental data and the weather forecast data.
[007] Embodiments in accordance with the present invention further provide a method for autonomous solar-powered drip irrigation. The method comprising steps of generating electrical energy using a photovoltaic panel adapted to convert solar energy; storing excess electrical energy in a battery storage unit adapted to provide power during periods of low or no sunlight; regulating charging and discharging of the battery storage unit using a charge controller adapted to prevent overcharging and over-discharging; collecting environmental data using a plurality of sensors adapted to measure environmental parameters; processing the collected environmental data and weather forecast data using a controller configured to execute an artificial intelligence (AI) model; and controlling a water distribution subsystem to distribute water through a drip irrigation network via one or more pumps and valves based on the processed environmental data and the weather forecast data.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a solar-powered autonomous drip irrigation system.
[009] Next, embodiments of the present application may provide a drip irrigation system that operates on solar energy with integrated energy storage.
[0010] Next, embodiments of the present application may provide a drip irrigation system that ensures uninterrupted irrigation even during cloudy conditions or nighttime.
[0011] Next, embodiments of the present application may provide a drip irrigation system that enables real-time analysis of soil moisture, weather data, and crop requirements, resulting in precise water distribution and improved crop yield.
[0012] Next, embodiments of the present application may provide a drip irrigation system that minimizes human intervention.
[0013] Next, embodiments of the present application may provide a drip irrigation system that reduces labor costs, eliminates manual errors, and ensures consistent irrigation efficiency.
[0014] Next, embodiments of the present application may provide a drip irrigation system that offers scalability for both small farms and large agricultural fields, making it adaptable for diverse agricultural needs.
[0015] Next, embodiments of the present application may provide a drip irrigation system that allows remote monitoring, instant alerts, and manual override through mobile or web applications.
[0016] Next, embodiments of the present application may provide a drip irrigation system that provides farmers with greater control and convenience.
[0017] These and other advantages will be apparent from the present application of the embodiments described herein.
[0018] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0020] FIG. 1 illustrates a solar-powered autonomous drip irrigation system, according to an embodiment of the present invention; and
[0021] FIG. 2 depicts a flowchart of a method for autonomous solar-powered drip irrigation, according to an embodiment of the present invention.
[0022] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0023] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0024] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0025] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0026] FIG. 1 illustrates a solar-powered autonomous drip irrigation system 100 (hereinafter referred to as the drip irrigation system 100), according to an embodiment of the present invention. In an embodiment of the present invention, the drip irrigation system 100 may be integrated with solar power, artificial intelligence (AI), Internet of Things (IoT), and sensor-based automation in order to maximize water usage, increase crop yield, and reduce human interference. The drip irrigation system 100 may be formulated to be suitable for productive application in agricultural farms, greenhouses, and gardens, depending on real-time environmental data and forecast-based analytics-based dynamic water flow adjustment. The drip irrigation system 100 may further allow real-time monitoring and optimization of irrigation parameters while optimizing water utilization, maximizing production of crops, and reducing the human intervention.
[0027] According to the embodiments of the present invention, the drip irrigation system 100 may incorporate non-limiting hardware components to enhance a processing speed and an efficiency such as the drip irrigation system 100 may comprise a photovoltaic panel 102, a battery storage unit 104, a charge controller 106, a plurality of sensors 108, a soil moisture sensor 110, a humidity and temperature sensor 112, a flow sensor 114, a water distribution subsystem 116, a drip irrigation network 118, a controller 120, an Internet-of-Things (IoT) communication unit 122, an external source 124, an artificial intelligence (AI) model 126, a cloud server 128, a user device 130, and a user interface 132. In an embodiment of the present invention, the hardware components of the drip irrigation system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing drip irrigation systems.
[0028] In an embodiment of the present invention, the photovoltaic panel 102 may be adapted to generate electrical energy. The photovoltaic panel 102 may be, but not limited to, a monocrystalline panel, a polycrystalline panel, a thin-film panel, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the photovoltaic panel 102, including known, related art, and/or later developed technologies.
[0029] In an embodiment of the present invention, the battery storage unit 104 may be adapted to store excess energy received from the photovoltaic panel 102. The excess energy may be stored during peak sun hours for use in demand periods.
[0030] In an embodiment of the present invention, the charge controller 106 may be adapted to regulate charging and discharging of the battery storage unit 104. The charge controller 106 may be adapted to regulate energy flow to prevent overcharging or over-discharging and hence achieve optimal utilization of energy. The energy stored may be utilized to supply the various components of the drip irrigation system 100 with a consistent rate of operation.
[0031] In operation, the photovoltaic panel 102 may capture solar radiation and convert it into electrical energy, which is then supplied to the charge controller 106. The charge controller 106 may regulate the flow of energy, directing a portion for immediate use by the drip irrigation system 100 and diverting excess energy to the battery storage unit 104 for later utilization. During periods of low or no solar radiation, such as cloudy conditions or nighttime, the stored energy in the battery storage unit 104 may be discharged under the supervision of the charge controller 106 to maintain uninterrupted operation of the pumps, valves, sensors, and the controller 120. In this manner, a combined operation of the photovoltaic panel 102, the battery storage unit 104, and the charge controller 106 may provide a reliable, autonomous, and energy-efficient power supply for the drip irrigation system 100 for reducing dependency on external grid electricity and ensuring sustainable irrigation practices.
[0032] In an embodiment of the present invention, the plurality of sensors 108 may be adapted to collect environmental data. The plurality of sensors 108 may comprise the soil moisture sensor 110, the humidity and temperature sensor 112, and the flow sensor 114. The environmental data may be a moisture level of soil, a humidity and temperature level of the soil, and a flow of running water. The environmental data may be collected in real-time. Upon collection, the environmental data may be relayed to the artificial intelligence (AI) model 126.
[0033] The soil moisture sensor 110 may be adapted to measure the moisture level of the soil. The soil moisture sensor 110 may further be adapted to measure water levels at different levels. The humidity and temperature sensor 112 may be adapted to measure the ambient humidity and temperature level.
[0034] In some embodiments of the present invention, the soil moisture sensor 110 may include capacitive, resistive, or time-domain reflectometry (TDR)-based technologies, or any other known or later-developed sensor technologies. The humidity and temperature sensor 112 may be adapted to measure the ambient humidity and temperature level. In some embodiments of the present invention, the humidity and temperature sensor 112 may be further adapted to detect microclimate variations within different sections of the field, thereby allowing localized irrigation control. In addition, the humidity and temperature sensor 112 may integrate dew point detection, frost prediction, or heat stress monitoring functionalities, enabling the AI model 126 to optimize irrigation timing in response to specific plant stress conditions.
[0035] The flow sensor 114 may be configured to measure a flow of the running water. The flow sensor 114 may be configured to detect anomalies such as, but not limited to, water leakage, pipe blockage, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the anomalies, including known, related art, and/or later developed technologies. The anomalies detected may further be relayed to the artificial intelligence (AI) model 126.
[0036] In an embodiment of the present invention, the water distribution subsystem 116 may be adapted to deliver water through one or more of pumps, valves, and drip lines to the drip irrigation network 118. The water distribution subsystem 116 may be adapted to utilize the electrical energy generated by the photovoltaic panel 102. The water distribution subsystem 116 may control the accurate volume of flow of water to sections of the field, thus dispensing accurate volumes of water according to actual-time calculations of the artificial intelligence (AI) model 126. In some embodiments, the water distribution subsystem 116 may further comprise flow meters, pressure regulators, and electrically actuated solenoid valves to dynamically adjust the water flow rate and distribution pattern based on soil moisture levels, crop type, plant growth stage, and predicted weather conditions. In yet another embodiment, the water distribution subsystem 116 may include a feedback loop mechanism, wherein the sensors 108 continuously provide soil and environmental parameters to the controller 120, enabling closed-loop control of irrigation operations for enhanced precision and water conservation.
[0037] In an embodiment of the present invention, the drip irrigation network 118 may be adapted to receive water from the water distribution subsystem 116. The drip irrigation network 118 may be adapted to supply dripping water to plantations using primary drip lines, secondary drip lines, microtubes, and emitters. The emitters may be pressure-compensating or non-pressure-compensating, depending on the irrigation design requirements. In some embodiments, the drip irrigation network 118 may be configured to vary the drip rate for different field zones to enable customized irrigation for heterogeneous soil profiles and crop species within the same cultivation area. In other embodiments of the present invention, the drip irrigation network 118 may incorporate clog-resistant emitters and self-flushing mechanisms to ensure uninterrupted and efficient water delivery. Embodiments of the present invention are intended to include or otherwise cover any type of drip lines, including known, related art, and/or later developed technologies, including but not limited to subsurface drip irrigation lines and smart irrigation emitters embedded with miniature sensors for flow and blockage detection.
[0038] In an embodiment of the present invention, the controller 120 may be connected to the plurality of sensors 108 and the water distribution subsystem 116. The controller 120 may be configured to receive the collected environmental data from the plurality of sensors 108.
[0039] The controller 120 may be configured to process the environmental data and weather forecast data using the artificial intelligence (AI) model 126. The weather forecast data may be fetched from the external source 124. In some embodiments of the present invention, the controller 120 may employ one or more data acquisition algorithms, such as application programming interface (API) integration algorithms, web scraping algorithms, or message queue protocols, to automatically fetch real-time weather forecast data from trusted meteorological databases, satellite-based services, or Internet of Things (IoT)-enabled weather stations. The weather forecast data may enable the controller 120 to pre-emptively adjust irrigation schedules.
[0040] The irrigation schedules may allow scheduling of irrigation in advance; thus, wasteful watering ahead of rain or top-up irrigation amid drought may be avoided. The weather forecast data may be, but not limited to, rainfall prediction, temperature prediction, humidity prediction, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the weather forecast data, including known, related art, and/or later developed technologies.
[0041] For example, if irrigation is scheduled for 8:00 AM in the morning but the weather forecast data indicates a high probability of rainfall at 9:00 AM, the controller 120 may automatically postpone or cancel the scheduled irrigation to prevent unnecessary water consumption. Alternatively, if the forecast data indicates only a light drizzle that may not sufficiently replenish soil moisture, the controller 120 may reduce the irrigation volume rather than cancel it completely, thereby ensuring optimal hydration without waste. In another instance, during prolonged drought conditions, the controller 120 may schedule supplementary “top-up” irrigation cycles at intervals predicted by the artificial intelligence (AI) model 126 to maintain soil moisture within the optimal range for crop growth. Such intelligent decision-making, driven by real-time and forecasted environmental conditions, enables the system 100 to deliver highly adaptive and efficient irrigation management.
[0042] The controller 120 may be configured to actuate the water distribution subsystem 116 to distribute water through the drip irrigation network 118 via the one or more of the pumps and the valves based on the processed environmental data and the weather forecast data.
[0043] The controller 120 may be configured to optimize irrigation schedules by executing a machine learning algorithm trained on historical environmental data and irrigation outcomes. The historical environmental data and irrigation outcomes may be retrieved from the external source 124.
[0044] The artificial intelligence (AI) model 126 may be a machine learning algorithm-based control system. The artificial intelligence (AI) model 126 may be trained based on the environmental data and weather forecast data, and with assistance of the plurality of sensors 108. The artificial intelligence (AI) model 126 dynamically adjusts the irrigation schedule based on real-time sensor readings, historical behavioral patterns, and predicted weather. The artificial intelligence (AI) model 126 learns to become more proficient at making choices with passage of time. The artificial intelligence (AI) model 126 maintains the water supply levels at suitable levels based on environmental factors, crop demand, and soil moisture levels to avoid wasting it and achieve optimum results.
[0045] For example, during the summer season, when ambient temperatures are high and evaporation rates are accelerated, the artificial intelligence (AI) model 126 may schedule more frequent but shorter irrigation cycles in the early morning or late evening hours to minimize water loss due to evaporation while ensuring adequate soil moisture for crop growth. Conversely, during the winter season, when ambient temperatures are lower and soil moisture retention is naturally higher, the artificial intelligence (AI) model 126 may reduce the frequency of irrigation cycles and extend the interval between watering events. In some embodiments, the AI model 126 may further integrate frost prediction data during winter to avoid unnecessary irrigation when soil freezing is anticipated. In this manner, the system dynamically adapts to seasonal variations, ensuring optimized water usage and sustainable irrigation management across varying climatic conditions.
[0046] In an embodiment of the present invention, the Internet-of-Things (IoT) communication unit 122 may be configured to relay alerts generated by the controller 120 to the user device 130. The Internet-of-Things (IoT) communication unit 122 may be a Wireless Fidelity (Wi-Fi) enabled communication module. The alerts may be relayed using the cloud server 128. The alerts may be, but not limited to, low water supply, pump malfunction, sensor failure, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the alerts, including known, related art, and/or later developed technologies.
[0047] For example, the alerts generated and relayed by the Internet-of-Things (IoT) communication unit 122 may be a low water supply alert to notify the user when the water source level falls below a predefined threshold. Similarly, the alerts generated and relayed by the Internet-of-Things (IoT) communication unit 122 may be a pump malfunction to indicate an abnormal motor current, overheating, or failure to maintain expected discharge pressure.
[0048] In addition, the system 100 may generate a battery status alert to warn the user of low charge levels in the battery storage unit 104 or charging irregularities managed by the charge controller 106. Furthermore, weather-based advisory alerts may be provided to suggest postponement of irrigation due to predicted rainfall or to recommend top-up irrigation during a heatwave or drought condition. In some embodiments, such alerts may be communicated to the user device 130 in the form of push notifications, short message service (SMS) messages, or email notifications, thereby enabling the user to remotely monitor and manage the drip irrigation system 100 in real time.
[0049] In an embodiment of the present invention, the user device 130 may be adapted to receive alerts from the communication unit 122. The user device 130 may comprise the user interface 132. The alerts received by the user device 130 may be displayed on the user interface 132. The user interface 132 of the user device 130 may enable the user to override parameters manually and alter irrigation timing according to requirements. The user interface 132 of the user device 130 may enable the user to view real-time soil moisture, weather, and irrigation status information remotely. The user device 130 may be Internet-of-Things (IoT) enabled. The user interface 132 may be a web portal, a mobile application, and so forth.
[0050] FIG. 2 depicts a flowchart of a method 200 for autonomous solar-powered drip irrigation, according to an embodiment of the present invention.
[0051] At step 202, the drip irrigation system 100 may generate the electrical energy using the photovoltaic panel 102 adapted to convert the solar energy.
[0052] At step 204, the drip irrigation system 100 may store the excess electrical energy in the battery storage unit 104 adapted to provide power during periods of low or no sunlight.
[0053] At step 206, the drip irrigation system 100 may regulate charging and discharging of the battery storage unit 104 using the charge controller 106 adapted to prevent overcharging and over-discharging.
[0054] At step 208, the drip irrigation system 100 may collect the environmental data using the plurality of sensors 108 adapted to measure the environmental parameters.
[0055] At step 210, the drip irrigation system 100 may process the collected environmental data and the weather forecast data using the controller 120 configured to execute the artificial intelligence (AI) model 126.
[0056] At step 212, the drip irrigation system 100 may control the water distribution subsystem 116 to distribute water through the drip irrigation network 118 via one or more of the pumps and the valves based on the processed environmental data and the weather forecast data.
[0057] At step 214, the drip irrigation system 100 may fetch the weather forecast data from the external source 124 to pre-emptively adjust the irrigation schedules.
[0058] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0059] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal language of the claims. , Claims:CLAIMS
I/We Claim:
1. A solar-powered autonomous drip irrigation system (100), the drip irrigation system (100) comprising:
a photovoltaic panel (102) adapted to generate electrical energy;
a battery storage unit (104) adapted to store excess energy received from the photovoltaic panel (102);
a charge controller (106) adapted to regulate charging and discharging of the battery storage unit (104);
a plurality of sensors (108) adapted to collect environmental data;
a water distribution subsystem (116) adapted to deliver water through one or more pumps, valves, and drip lines, wherein the water distribution subsystem (116) is adapted to utilize the electrical energy generated by the photovoltaic panel (102); and
a controller (120) communicatively connected to the plurality of sensors (108) and the water distribution subsystem (116), characterized in that the controller (120) is configured to:
receive the collected environmental data from the plurality of sensors (108);
process the environmental data and weather forecast data using an artificial intelligence (AI) model (126); and
actuate the water distribution subsystem (116) to distribute water through a drip irrigation network (118) via the one or more pumps and valves based on the processed environmental data and the weather forecast data.
2. The drip irrigation system (100) as claimed in claim 1, wherein the controller (120) is configured to optimize irrigation schedules by executing a machine learning algorithm trained on historical environmental data and irrigation outcomes.
3. The drip irrigation system (100) as claimed in claim 1, wherein the controller (120) is configured to fetch the weather forecast data from an external source (124) to pre-emptively adjust irrigation schedules.
4. The drip irrigation system (100) as claimed in claim 1, wherein the controller (120) is configured to detect anomalies selected from water leakage, pipe blockage, or a combination thereof, using a flow sensor (114).
5. The drip irrigation system (100) as claimed in claim 1, wherein the controller (120) is configured to transmit alerts selected from low water supply, pump malfunction, sensor failure, or a combination thereof, via an Internet-of-Things (IoT) communication unit (122).
6. The drip irrigation system (100) as claimed in claim 1, wherein the water distribution subsystem (116) is adapted to deliver irrigation through the drip irrigation network (118) selected from primary drip lines, secondary drip lines, or a combination thereof.
7. The drip irrigation system (100) as claimed in claim 1, wherein the controller (120) is configured to adjust irrigation schedules based on weather forecast data selected from rainfall prediction, temperature prediction, humidity prediction, or a combination thereof.
8. The drip irrigation system (100) as claimed in claim 1, wherein the photovoltaic panel (102) is selected from a monocrystalline panel, a polycrystalline panel, a thin-film panel, or a combination thereof.
9. A method (200) for autonomous solar-powered drip irrigation, the method (200) is characterized by steps of:
generating electrical energy using a photovoltaic panel (102) adapted to convert solar energy;
storing excess electrical energy in a battery storage unit (104) adapted to provide power during periods of low or no sunlight;
regulating charging and discharging of the battery storage unit (104) using a charge controller (106) adapted to prevent overcharging and over-discharging;
collecting environmental data using a plurality of sensors (108) adapted to measure environmental parameters;
processing the collected environmental data and weather forecast data using a controller (120) configured to execute an artificial intelligence (AI) model (126); and
controlling a water distribution subsystem (116) to distribute water through a drip irrigation network (118) via one or more pumps and valves based on the processed environmental data and the weather forecast data.
10. The method (200) as claimed in claim 9, comprising a step of fetching the weather forecast data from an external source (124) to pre-emptively adjust irrigation schedules.

Date: October 08, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant