Age Classification of White-tailed Deer Via Computer Vision and Deep Learning

Accurate age estimation of wild whitetail deer remains a significant challenge for wildlife management. This study presents the first application of computer vision to whitetail buck age estimation using trail camera imagery, evaluating over sixty classification algorithms from traditional machine learning to advanced deep learning techniques. Our approach utilizes transfer learning and CNN ensembles to achieve a breakthrough cross-validation accuracy of 76.7 {+/-} 5.9%, substantially outperforming traditional classifiers (57%), human expert assessment (60.6%), and morphometric methods (63%), and surpassing the 70% accuracy threshold required by professionals for wildlife management decisions. Furthermore, attention map analysis of the ResNet-18 ensemble reveals that the model learns to focus on the same anatomical features (neck, chest, and stomach regions) that human experts rely upon for age assessment. This biological validation demonstrates that the CNN identifies genuine age-related morphological changes rather than spurious correlations, lending credibility to its predictions. This breakthrough offers wildlife professionals a practical tool to dramatically reduce manual age assessment workload while exceeding current accuracy standards, potentially transforming how deer populations are monitored and managed across North America.