MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202621096691 A) filed by Rajarambapu Institute Of Technology on August 11, 2026, for Artificial Intelligence-Based Multi-Modal Precision Agriculture System With Adaptive Disease Prediction And Risk Assessment.
Inventors include Prof. Arati R. Gavade; Mr. Rohan K. Gavade; Prof. Shweta S. Patil; Dr. Sandip S. Sathe; Prof. Krunal B. Pawar; Dr. Varsha T. Lokare; Prof. Jayashree A. Patil; Dr. Akshay A. Satpute; and Dr. Ramesh M. Badiger.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: Artificial Intelligence-Based Multi-Modal Precision Agriculture System with Adaptive Disease Prediction and Risk Assessment This invention describes an intelligent precision agriculture system and method for early prediction, detection, severity assessment, and management of plant diseases using adaptive image acquisition, environmental sensing, and artificial intelligence. The system integrates an image acquisition module, environmental sensor module, edge processing unit, Adaptive Image Acquisition Algorithm (AIAA), Dynamic Disease Risk Index (DDRI) computation module, multi-modal sensor fusion engine, disease detection module, disease severity estimation module, recommendation engine, cloud analytics platform, and mobile application. The Adaptive Image Acquisition Algorithm dynamically modifies image acquisition frequency according to environmental conditions, crop growth stage, historical disease information, and continuously updated disease risk. The Dynamic Disease Risk Index combines environmental measurements, image-derived features, historical disease records, weather forecasts, and crop information to predict disease occurrence before visible symptoms appear. The invention further generates disease severity estimates, explainable artificial intelligence outputs, and customized agricultural recommendations while supporting edge-based operation under intermittent network connectivity to improve crop productivity, optimize resource utilization, and reduce unnecessary pesticide application.
Disclaimer: Curated by HT Syndication.
