MUMBAI, India, Feb. 13 -- Intellectual Property India has published a patent application (202541124465 A) filed by Nandha Engineering College, Erode, Tamil Nadu, on Dec. 10, 2025, for 'smart borewell water management system.'
Inventor(s) include S Ramesh; K Mytheryan; T Kartheeshwar; and Kshirsagar Aum.
The application for the patent was published on Feb. 13, under issue no. 07/2026.
According to the abstract released by the Intellectual Property India: "The present invention introduces a Smart Borewcll Water Management System, known as Hydro Vision AI, designed to monitor and predict borewell water levels using a combination of loT sensors and artificial intelligence. The fSYStem integrates DHT sensors, soil moisture sensors, and an Al-enabled camera to collect accurate environmental and !water-related data. This information is processed through an loT controller and analyzed using AI algorithms to determine eal-time water levels, detect abnormal conditions, and forecast future groundwater availability. [0031) The processed insights are displayed on a user-friendly dashboard or mobile application, allowing users to access ive readings, alerts, and predictive trends from any location. By automating borewell monitoring and motor control, the system prevents dry-run damage, reduces manual effort, saves energy, and promotes responsible groundwater management. The invention is suitable for agricultural fields, households, and industrial applications where efficient and sustainable water usage is essential. [0032) The invention integrates multiple smart sensors such as DHT11/DHT22 sensors (for temperature and humidity), soil moisture sensors, and anAl-enabled camera module to capture real-time environmental and water-level information. lfhese sensors are installed in and around the borewell structure to continuously cullt:ct accurate data related to humidity, emperature, soil moisture, and visible water conditions. The AI camera monitors visual changes inside the borewell for better prediction accuracy. The system enables remote monitoring via mobile apps, dashboards, or SMS alerts, giving farmers and water managers real-time information regarding water availability and pump health. By forecasting groundwater levels, the system helps in planning irrigation schedules, avoiding pump damage, and ensuring sustainable use ofborewell water resources. (0033) The AI engine employs machine-learning models to determine real-time borewell water levels, identify abnormal patterns such as rapid depletion, pump dry-run conditions, or environmental inconsistencies, and generate short-term and long-term predictions of future groundwater availability. The system further incorporates communication interfaces enabling remote data visualization and alert notifications through mobile or web applications. By integrating multi-sensor data acquisition with AI-driven predictive analytics, the invention provides a robust, autonomous, and scalable solution for managing borewell water resources, optimizing irrigation schedules, preventing pump damage, and supporting sustainable groundwater usage. The invention offers improved accuracy, operational efficiency, and reliability compared o conventional borewell monitoring methods. [0034) The invention further pertains to the technical fields of automated groundwater assessment, remote sensing, and intelligent resource management. Existing borewell systems lack real-time predictive capabilities and depend heavily on manual inspection, which often results in pump failures, water wastage, and inaccurate groundwater estimation. The pisclosed invention overcomes these limitations by integrating multi-modal sensing technologies with AI-based analytics o deliver continuous, high-precision monitoring. Through seamless interfacing of environmental. sensors, optical detection units, and machine-learning models, the system establishes a unified platform capable of autonomously 'nterpreting complex groundwater behavior. During operation, the loT controller periodically acquires sensor readings and image streams from the borewell environment and applies calibration, filtering, and noise-reduction techniques before ransmitting the processed data to the AI module. The predictive engine evaluates the incoming data using trained ~lgorithms that model groundwater fluctuations based on historical datasets, climatic variables, soil conditions, and ~etected visual changes."
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