Intellectual Property India Publishes Patent Application for An Intelligent Leaf Image-Based Plant Disease Detection And Pesticide Recommendation System Using Deep Learning

MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641096405 A) filed by Velammal Institute Of Technology; Mr. Retheesh B; Mr. Sathiya Jabasundar; Dr. Venkadesh R; Gokul Raj V; Jagadeesan S; Kumaresan V; and Manikandan V on August 10, 2026, for An Intelligent Leaf Image-Based Plant Disease Detection And Pesticide Recommendation System Using Deep Learning.

Inventors include Mr. Retheesh B; Mr. Sathiya Jabasundar; Dr. Venkadesh R; Gokul Raj V; Jagadeesan S; Kumaresan V; and Manikandan V.

The application for the patent was published on August 14, 2026, under issue no. 33/2026.

Abstract: Disclosed herein is an autonomous phytopathological surveillance and treatment prescription system integrating a field-deployed IoT hardware layer with a deep Convolutional Neural Network (CNN) diagnostic engine and a Context-Aware Dosage Prescription Engine (CADPE). Distributed ESP32-CAM microcontroller nodes autonomously capture crop foliar imagery at configurable intervals, transmitting images over a secured wireless network to a centralised processing server. Received images undergo spatial standardisation, radiometric normalisation, and background suppression preprocessing before traversing the CNN encoder, which extracts discriminative hierarchical phytopathological features enabling classification across 69 or more plant disease categories. A Softmax classification head produces calibrated disease probability scores; predictions meeting a configurable confidence threshold trigger CADPE, which retrieves the approved agronomic treatment protocol and computes a proportional chemical or biological treatment volume scaled to the operator-supplied land acreage. Predictions below the confidence threshold are routed to an anomaly audit log for expert review. A real-time web-based supervisory dashboard aggregates live acquisition feeds, diagnostic outputs, and CADPE prescriptions for farm operators and agronomic advisors. Keywords: Convolutional Neural Network, Precision Agriculture, Phytopathological Detection, Internet of Things, ESP32-CAM, Context-Aware Dosage Prescription, Deep Learning, Smart Farming, Autonomous Surveillance, Pesticide Recommendation

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