Intellectual Property India Publishes Patent Application for 'Quantum-Enhanced Deep Learning System For Real-Time Automated Detection And Monitoring Of Crop Health Conditions' Filed by Jaydeep Lokhande; Shamla Mantri; Samruddhi Sapkal; Prof. Shobha Prabhakar Bamane; Akshada Ravindra Patil; Mr. Vipin Gangadhar Puranik; Shruti O. Sarode; Soniya Shitalkumar Waghmare; Dr. Sarika Jadhav; and Suvarna A. Bahir

MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202621042852 A) filed by Jaydeep Lokhande; Shamla Mantri; Samruddhi Sapkal; Prof. Shobha Prabhakar Bamane; Akshada Ravindra Patil; Mr. Vipin Gangadhar Puranik; Shruti O. Sarode; Soniya Shitalkumar Waghmare; Dr. Sarika Jadhav; and Suvarna A. Bahir, Pune, Maharashtra, on April 3, for 'quantum-enhanced deep learning system for real-time automated detection and monitoring of crop health conditions.'

Inventor(s) include Jaydeep Lokhande; Shamla Mantri; Samruddhi Sapkal; Prof. Shobha Prabhakar Bamane; Akshada Ravindra Patil; Mr. Vipin Gangadhar Puranik; Shruti O. Sarode; Soniya Shitalkumar Waghmare; Dr. Sarika Jadhav; and Suvarna A. Bahir.

The application for the patent was published on May 29, under issue no. 22/2026.

According to the abstract released by the Intellectual Property India: "A system integrating quantum circuits with deep learning models for automated crop health monitoring. The system utilizing multi-modal agricultural data including imagery, sensor, and environmental datasets for analysis. A hybrid quantum-classical architecture enhancing feature extraction and classification accuracy in crop health detection. The system employing variational quantum circuits for optimizing deep learning model parameters efficiently. A real-time monitoring module providing continuous crop health assessment using IoT-enabled sensor networks. The system detecting diseases, nutrient deficiencies, and pest infestations using advanced image processing techniques. A scalable framework deployable across diverse agricultural environments including farms, greenhouses, and plantations. The system generating predictive insights and automated alerts for improved agricultural decision-making processes. Integration of cloud and edge computing for efficient data processing and low-latency system performance. A method reducing manual intervention by automating crop monitoring using intelligent quantum-enhanced deep learning models."

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