Tricho-Vision: The use of computer vision in trichotaxonomy for enhancing wildlife conservation of priority species

Abstract

Mammalian hair serves as a critical biological marker, aiding species identification essential for wildlife conservation and crime control. This study introduces the first extensive benchmark for classifying microscopic images of mammal hair from species prioritized for conservation. Our goal is to develop standardized methods, metrics, and best practices for utilizing advanced computer vision techniques, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Swin Transformers, to classify hair samples across Order, Family, Genus and Species taxonomic levels. We present a novel dataset of 76 species, including critically endangered and endangered species, curated specifically for this classification challenge. The methodology integrates automated feature extraction of cuticle patterns and medulla structures, enabling high-precision species differentiation. Our findings demonstrate that Swin Transformer-based models outperform traditional CNNs and ViTs across taxonomic levels, with techniques like image cropping further improving classification accuracy by diversifying the training set. The proposed Tricho-Vision framework offers significant applications in biodiversity monitoring and wildlife crime investigation, facilitating accurate species identification from forensic hair samples. Additionally, we introduce a interactive tool for real-time taxonomic classification, showcasing the practical utility of our research and fostering broader interdisciplinary engagement in conservation science and forensic applications.