MUMBAI, India, March 13 -- Intellectual Property India has published a patent application (202641025621 A) filed by Dayananda Sagar College Of Engineering, Bangalore, Karnataka, on March 4, for 'quantum-integrated multi-layer feature fusion system for high-accuracy pest classification using adaptive quantum kernel mapping.'
Inventor(s) include Dr. Ramya R S; Dr. Samyama Gunjal G H; Dr. Anusha Preetham; Dr. Pavithra G; Dr. Swapnil S. Ninawe; Akash Ashok Nayak; and Guruprasad Rachayya Hiremath.
The application for the patent was published on March 13, under issue no. 11/2026.
According to the abstract released by the Intellectual Property India: "In modern farming practices it is necessary to detect the pests at the initial stages of the crop growth to avoid the spoilage of the forthcoming harvest. The main challenges included in the pest detection are overcoming similarity features between particular pests and complexity in the natural backgrounds. Various AIML models have been implemented so far but the major hindrance is that they lack certain computational power which cannot be used on low power agricultural devices. This research introduces a new technique to detect the pests by using the QSVC (quantum support vector classification) with a new adaptive multilayer quantum feature system (AML-QFF) concepts of quantum computing. For quantum feature encoding this approach uses hierarchical variational circuits which leads to richer representations in the Hilbert space, emphasizing the textural variations and shapes of the pest. The data used here is the Pestopia data set and approach proposed performs way far better than that of the various AIML models implemented until now. The proposed solutions combines AML-QFF with QSVC to detect and predict the agricultural pests."
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