MUMBAI, India, Sept. 7 -- Intellectual Property India has published a patent application (202641104224 A) filed by St. Peters Engineering College Autonomous on August 29, 2026, for Ai-Iot Enabled Automated System For Agricultural Crop Protection And Wild Animal Intrusion Detection.
Inventors include Dr. P. Deepan; Mr. G. Srujan Reddy; Mr. Ch. V. Ganesh; Mr. V. Santhosh Kumar; Mrs. Durga Sravanthi Yedidi; Mr. P. Bharath Kumar; Mrs. P. Amaleswari; Mr. Kvvb. Durgaprasad; Mrs. D. Divya; Mr. L. Surender; and Mr. B. Bharath Kumar.
The application for the patent was published on September 04, 2026, under issue no. 36/2026.
Abstract: ABSTRACT Agricultural crops are frequently affected by wild animal intrusion, particularly in areas located near forests and wildlife habitats, resulting in crop damage, economic losses, and reduced farmer productivity. Conventional protection methods such as manual surveillance, fencing, loud noises, and physical guarding require continuous human effort and may not provide timely detection of approaching animals. To address these challenges, an AI-IoT Enabled automated system for Agricultural Crop Protection and Wild Animal Intrusion Detection is proposed. The system integrates AI, various IoT sensors like camera sensors, motion sensors, acoustic sensors, infrared sensors, environmental sensors, GPS, and GSM/IoT communication modules to provide continuous and intelligent monitoring of agricultural fields. Camera and motion sensors capture real-time information from the field, while AI-based image and pattern analysis identifies and classifies potential wild animals and distinguishes them from normal environmental activities. Upon detecting an intrusion, the system automatically activates appropriate non-harmful deterrent mechanisms such as lights, alarms, acoustic signals, and other field protection devices to discourage animals from entering the crop area. At the same time, the system generates real-time notifications containing the intrusion status and field location and transmits them to the farmer or authorized personnel through a mobile or cloud- based platform. The collected data can also be stored and analyzed to identify recurring intrusion patterns and support predictive crop protection decisions. The proposed automated system reduces the need for continuous manual surveillance, enables early detection of wild animal movement, minimizes crop losses, improves farmer safety, and supports efficient and sustainable agricultural practices.
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