I-Corps: Translation Potential of an Artificial Intelligence System for Wildlife Detection and Deterrence for Protecting Plants and Wildlife

This I-Corps project focuses on the development of an intelligent, non-lethal wildlife management system that enables coexistence between humans and wildlife in urban and rural environments. As human expansion encroaches on natural habitats, conflicts with wildlife result in property damage, safety risks, and ecological imbalances. Traditional deterrence methods, such as fencing or chemical repellents, are costly, ineffective, or environmentally harmful. This project introduces an adaptive, technology-driven approach using artificial intelligence and sound-based deterrence to create virtual boundaries that protect crops, gardens, and other sensitive areas while minimizing harm to wildlife. The potential commercial impact spans residential, agricultural, and commercial applications, offering cost-effective, scalable, and sustainable solutions for human-wildlife interactions. By integrating intelligent monitoring and deterrence, this innovation supports conservation efforts, promotes public safety, and provides a marketable solution to stakeholders seeking humane and efficient wildlife management strategies. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an artificial intelligence (AI)-driven framework that integrates real-time vision control, acoustic redirection, and cloud-based learning. The system employs computer vision to detect and identify wildlife species, combining this data with targeted ultrasonic deterrence to influence animal behavior without causing harm. The intelligent coexistence platform continuously adapts through cloud-connected models that refine deterrence strategies based on real-world interactions. The research supporting this technology builds advances in AI, edge computing, and acoustic engineering, demonstrating promising results in controlled laboratory conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.