Dental Age Assessment of White-tailed Deer via Computer Vision and Deep Learning

Abstract

Accurate age estimation of white-tailed deer remains challenging for wildlife management, with existing computer vision methods limited to trail camera imagery and manual dental analysis requiring specialized expertise. This research presents the first computer vision approach to dental age assessment of white-tailed deer, eliminating the need for manual tooth replacement and wear evaluation. A dataset of 243 jawbone images is used to develop a multi-fold Con-volutional Neural Network ensemble, achieving 90.7% {+/-} 2.6% accuracy through 5-fold cross-validation. Attention map analysis confirms the model locates and focuses exclusively on dental features rather than imaging artifacts, successfully identifying the same wear patterns and eruption sequences that wildlife biologists rely upon in manual assessments. This implementation substantially outperforms computer vision models based on trail camera imagery and meets the accuracy standards required for scientific research and population management. The automated approach developed in this study enables rapid, objective age determination from dental specimens, dramatically reducing analysis time while offering immediate practical value for wildlife agencies, research institutions, and hunting programs requiring precise age data from harvested deer.