High-Accuracy Animal Species Classification Using Transfer Learning with ResNet50 on Wildlife Camera Trap Images for Enhanced Conservation Efforts

Accurately identifying animal species from wildlife camera trap images is essential for conservation and biodiversity monitoring. However, manual processing is severely constrained by the massive volume of data collected by camera traps. Using transfer learning and the ResNet50 model, a high-accura cy animal species classification framework was offered as an approach to this challenge. Utilization of the iWildCam2020_256 dataset—a varied collection of wildlife photos taken in a range of ecosystems optimize ResNet50, a pre-trained neural network to improve its performance on this particular job. With the use of transfer learning approaches, this approach aims to maximize the model's feature extraction capabilities and make it capable of accurately differentiating between a large number of animal species. Compared with traditional techniques, the suggested framework significantly improved classification accuracy when trained and verified on the iWildCam2020_256 dataset. the application of data augmentation techniques and network hyperparameter tuning to reduce overfitting and enhance generalization in various contexts. The outcomes show how well the model can classify species in difficult situations, like changing lighting and partial occlusions. By offering a reliable instrument for automated species identification, our study advances the area of animal conservation by enabling more effective and scalable biodiversity assessments. With a 97% accuracy score, the suggested framework performs remarkably well and shows consistent species recognition across a wide range of animal images. It is essential to include the suggested framework into conservation initiatives, which would help with the precise and timely monitoring of wildlife populations—a crucial component in maintaining biodiversity.