MUMBAI, India, Sept. 7 -- Intellectual Property India has published a patent application (202641096693 A) filed by Dr. S. Karthiga on August 11, 2026, for System For Ai-Driven Smart Irrigation And Soil Resource Optimization.
Inventors include Dr. S. Karthiga; Dr. M. K. Kavitha Devi; and Gideonglady K.
The application for the patent was published on September 04, 2026, under issue no. 36/2026.
Abstract: The present invention discloses an artificial intelligence-driven smart irrigation and soil resource optimization system for adaptive, real-time management of irrigation across multiple agricultural zones. The system comprises a plurality of distributed soil-sensing nodes configured to measure soil parameters including moisture, temperature, pH, electrical conductivity, salinity, nutrient concentration, and soil compaction. The sensing nodes are operatively connected to a signal acquisition and conditioning module configured to amplify, filter, compensate, calibrate, and digitize the sensor signals. An edge computing unit comprising a processor, memory, local data storage, and an embedded artificial intelligence engine processes the soil data to generate zone- specific soil behaviour models, detect sensor drift, predict soil- moisture depletion, and calculate irrigation demand for each agricultural zone. A multi-zone irrigation arbitration engine prioritizes and allocates available water based on the calculated demand, crop requirements, environmental conditions, seasonal variations, and resource availability. An irrigation actuation module dynamically controls irrigation valves, pumps, flow regulators, drip lines, or sprinkler systems by adjusting water-flow rate, duration, frequency, and sequence. The system further supports hybrid LoRa and cellular communication, SMS-based farmer interaction, autonomous fail-safe operation during communication failure, local data buffering, and subsequent synchronization with a remote platform. The disclosed system enables adaptive sensor calibration, predictive irrigation scheduling, efficient water and energy utilization, reduced manual intervention, improved crop productivity, and sustainable agricultural resource management. Figures 1 and 2 illustrate the system architecture and operational method, respectively.
Disclaimer: Curated by HT Syndication.
