Harvesting Innovation: AI-Based Deer Re-Identification
- Length
- 1 hour
- Language
- English
- Event Format
- Virtual | Live
About The Live Webinar
White-tailed deer continue to challenge Pennsylvania agriculture, contributing to significant crop losses and economic impact across the region. As populations shift and traditional monitoring methods provide limited insight, landowners are seeking more consistent, data-driven tools to inform management decisions. In this Harvesting Innovation session, presenters from Nittany AI Advance will share findings from a student-led research project exploring the use of computer vision and machine learning to identify and re-identify individual white-tailed deer across images collected over time. The session will address practical considerations such as data availability, image variability, and seasonal changes, offering key lessons learned and recommendations for future monitoring efforts.
About the Series
The Harvesting Innovation Webinar Series explores how emerging technologies are transforming agriculture, energy, and natural systems. Held monthly, this dynamic series brings together innovators, educators, researchers, and industry leaders to highlight cutting-edge research and promote collaboration. Each session is designed to advance real-world solutions that improve efficiency, sustainability, and competitiveness across sectors.
This event is being offered at no charge to participants.
Space is limited - preregistration is required.
Registration is required to receive the link to access the webinar.
A link to the recorded webinar will be emailed to registrants within ten business days after the live event. The recording will be accessible for six months from the event date.
- Farmers, growers, processors, wholesalers, and distributors
- Agriculture, environment, and natural resources professionals
- Researchers and extension educators
- Undergraduate and graduate students
- Tech and engineering professionals in the fields of food, agriculture, environment, and natural resources
- Results from a student-led research project exploring how computer vision and machine learning can be used to identify and re-identify individual white-tailed deer
- Key considerations related to data availability, image variability, and seasonal changes that affect model performance
- Lessons learned and future recommendations from building, testing, and evaluating AI-based identification models
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