Intellectual Property India Publishes Patent Application for Iot-Enabled Machine Learning System For Paddy Leaf Disease Prediction And Yield Optimization

MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641086434 A) filed by Dr Yv Raghavarao; L. Poornimadevi; Konda Manoj Kumar; Subhashini V; Dr. Vinay Kumar; Dr. Rengima Nambiar; Dr. Pendurthy Anthony Sunny Dayal; R. Sathishkumar; Dr. L. Bhagya Lakshmi; Dr. Vidya Sagar S. D.; Dr D J Samatha Naidu; and Dr. P. Rajendran on July 15, 2026, for Iot-Enabled Machine Learning System For Paddy Leaf Disease Prediction And Yield Optimization.

Inventors include Dr Yv Raghavarao; L. Poornimadevi; Konda Manoj Kumar; Subhashini V; Dr. Vinay Kumar; Dr. Rengima Nambiar; Dr. Pendurthy Anthony Sunny Dayal; R. Sathishkumar; Dr. L. Bhagya Lakshmi; Dr. Vidya Sagar S. D.; Dr D J Samatha Naidu; and Dr. P. Rajendran.

The application for the patent was published on October 30, 2026, under issue no. 44/2026.

Abstract: IoT-Enabled Machine Learning System for Paddy Leaf Disease Prediction and Yield Optimization is the proposed invention. The proposed invention discloses an IoT- enabled machine learning system for paddy leaf disease prediction and yield optimization using a Spatio-Temporal Graph Attention Network (ST-GAT). The system integrates heterogeneous IoT sensor data, multispectral leaf imagery, weather observations, and crop growth information to construct a dynamic graph representation of the agricultural field. Each crop cluster is modeled as a graph node, while spatial and environmental relationships define the graph edges. The ST-GAT model learns disease propagation patterns by jointly analyzing spatial dependencies and temporal environmental variations, enabling early prediction of disease occurrence and severity before significant visual symptoms develop. A yield optimization engine further analyzes disease risk, nutrient availability, irrigation status, and weather conditions to generate adaptive recommendations for fertilizer application, irrigation scheduling, fungicide dosage, and harvesting time. Edge computing facilitates real- time processing, while cloud synchronization continuously improves prediction accuracy. The invention enhances crop productivity, reduces pesticide consumption, conserves water resources, and supports sustainable precision agriculture through intelligent, field-specific decision-making.

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