MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202621070335 A) filed by Dr. Bhausaheb B Musmade; Vedant Yogesh Kothawade; Siddhi Shivraj Sawant; and Vishwas Ulhas Godalkar on June 05, 2026, for Agricropnet: Ai-Driven Crop Disease Detection And Recommendation System For Precision Farming And Sustainable Agricultural Management.
Inventors include Dr. Bhausaheb B Musmade; Vedant Yogesh Kothawade; Siddhi Shivraj Sawant; and Vishwas Ulhas Godalkar.
The application for the patent was published on September 18, 2026, under issue no. 38/2026.
Abstract: ABSTRACT The present invention relates to AgriCropNet, an AI-driven crop disease detection and recommendation system for precision agriculture and sustainable crop management. The system is configured to capture crop images using a mobile camera, handheld scanner, drone camera, or field imaging unit and analyze the images through artificial intelligence models trained on crop disease datasets. The system detects visible symptoms such as leaf spots, wilting, discoloration, rust, blight, mildew, fruit rot, pest damage, and nutrient deficiency. The invention further integrates environmental and farm-related data, including temperature, humidity, soil moisture, rainfall, crop type, crop stage, location, and field history, to improve disease identification accuracy. A recommendation engine generates crop-specific advisory instructions based on disease type, severity, environmental condition, and suitable treatment options. The advisory may include pesticide usage, organic remedy, biological control, nutrient correction, irrigation adjustment, preventive practices, and expert consultation alerts. The system includes a farmer interface for multilingual text or voice guidance and a cloud-based expert dashboard for monitoring disease trends, validating cases, and generating regional outbreak alerts. The invention enables early disease detection, reduces unnecessary pesticide use, improves crop productivity, and supports data-driven agricultural decision-making.
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