MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641085384 A) filed by Sri Eshwar College Of Engineering on July 11, 2026, for Ai-Driven Digital Twin Model For Predictive Identification And Early Warning Of Human Wildlife Conflict Near Forest Villages.
Inventors include Dr. H. Anandakumar; Dr. R. Babitha Lincy; Ms. Minu Balakrishnan; and Ms. J. Yashwandra.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: Human–wildlife conflict has emerged as a serious issue in the forest fringe areas, causing the loss of human lives, damage to crops, depredation of livestock and jeopardising conservation of wildlife. Traditional monitoring methods include 'man on the ground' patrols, camera traps, or reporting systems that are reactive, and these methods are often not capable of timely warnings or precise anticipation of movement towards human habitation. This invention suggests the use of AI-driven Digital Twin model of predictive identification and early warning of human-wildlife conflict around forest villagers through synthesizing real-time data from IoT sensor networks, camera traps, acoustic sensors, GPS wildlife collars, unmanned aerial vehicle (UAV), satellite images and weather data. The Digital Twin performs continuous updates of a virtual representation of the forest ecosystem, corridors for animal movement, village boundaries and environmental conditions, and keeps them in sync with real-world information in the field. Multi-source information is fed into advanced Artificial Intelligence and deep learning models to help identify animal species, forecast animal movement, understand probability of conflict and detect abnormal animal behaviour. Edge AI helps to quickly analyze data locally and trigger alerts with low latency even in low connectivity environments and GIS based spatial analysis helps to visualize high risk areas and dynamic wildlife corridors. Should an increased conflict risk be identified, early warnings are automatically sent to forest officials, village communities and emergency response teams via mobile applications, SMS and monitoring dashboards. This proposed framework could potentially improve public safety and reduce crop and livestock losses, facilitate swift wildlife interventions, and foster sustainable coexistence between people and wildlife in areas bordering forests, by providing constant situational awareness, predictive risk assessment, and real-time decision support.
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