Intellectual Property India Publishes Patent Application for Deep Learning Based Iot Platform For Real-Time Plant Disease Diagnosis And Treatment Recommendation

MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202641105179 A) filed by Vels Institute Of Science, Technology & Advanced Studies Vistas on September 01, 2026, for Deep Learning Based Iot Platform For Real-Time Plant Disease Diagnosis And Treatment Recommendation.

Inventors include Dr. G. Gayathri; Dr. A. K. Kathireshan; Dr. P. Brindha Devi; Dr. Abinaya Ishwarya G K; Dr. R. Sridhar; Dr. S. Sivabalan; Dr. Sathishkumar G; Mr. Selvakumar Annamalai; Dr. K. Sasikala; Dr. S. Arunkumar; Dr. Meera Thangaraj; and Mrs. K. Parameswari.

The application for the patent was published on September 18, 2026, under issue no. 38/2026.

Abstract: ABSTRACT DEEP LEARNING BASED IOT PLATFORM FOR REAL-TIME PLANT DISEASE DIAGNOSIS AND TREATMENT RECOMMENDATION The present invention relates to an Internet of Things (loT) enabled deep learning platform for real-time plant disease diagnosis and treatment recommendation. The platform comprises a plurality of loT sensing units, an image acquisition unit, a communication module, a pre-processing module, a deep learning diagnosis engine, a disease-treatment recommendation engine and a user communication module. The loT sensing units acquire environmental and plant-condition parameters including temperature, relative humidity, soil moisture, soil condition and ambient light intensity, while the image acquisition unit captures real-time images of leaves, stems and/or fruits of a target plant. Based on the diagnosis, the disease-treatment recommendation engine correlates the plant species, disease condition, severity level and environmental conditions with a treatment knowledge base to generate an adaptive treatment recommendation comprising treatment type, quantity, application frequency, irrigation and/or nutrient requirements. The platform further provides real-time disease alerts and treatment recommendations to a remote user device and continuously updates the diagnosis and recommendation based on subsequently acquired plant images and loT sensor data, thereby enabling real-time, adaptive and precision-based plant disease management

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