MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641051218 A) filed by Dr. D. Jayanthi; G. A. John Bellarmine; Pranay V; Sanjana Madankumar; Jothilakshmi Rs; Sanjay Charan S; and Th Anushka B on April 22, 2026, for An Iot Enabled Intelligent System For Adaptive Irrigation And Rainwater Harvesting Using Machine Lea.
Inventors include Dr. D. Jayanthi; G. A. John Bellarmine; Pranay V; Sanjana Madankumar; Jothilakshmi Rs; Sanjay Charan S; and Th Anushka B.
The application for the patent was published on June 12, 2026, under issue no. 24/2026.
Abstract: Efficient water management and sustainable agricultural practices remain critical challenges in modem farming, particularly due to unpredictable climatic conditions, inefficient irrigation methods, and lack of real-time monitoring systems. Traditional irrigation techniques are often manual, resource-intensive, and unable to adapt dynamically to changing environmental conditions, resulting in water wastage, reduced crop productivity, and soil degradation. The proposed system, An loT-Enabled Intelligent System for Adaptive Irrigation and Rainwater Harvesting Using Machine Learning, introduces an advanced smart agriculture solution that integrates Internet of Things (IoT) sensors, machine learning using Gradient Boosting algorithms, and realtime data analytics to optimize irrigation and improve water resource utilization. The system incorporates environmental sensors such as soil moisture, temperature, humidity, and rainfall detection, along with ESP Cam-based visual monitoring and communication modules for continuous field monitoring and remote access. By analysing multi-parameter data, the machine learning model predicts optimal irrigation schedules and water requirements, enabling automated drip irrigation and intelligent rainwater harvesting. The system dynamically regulates water distribution, minimizes wastage, and enhances crop health and yield. Additionally, real-time alerts and monitoring dashboards support informed decision-making while reducing manual intervention. Overall, the proposed solution provides a scalable, cost-effective, and data- driven framework for precision agriculture, promoting sustainable farming practices and efficient water management across diverse agricultural environments.
Disclaimer: Curated by HT Syndication.
