Optimizing Animal Classification through Convolutional Neural Networks

Abstract

Efficient and accurate wildlife monitoring in their natural habitats is crucial for conservation efforts. This project introduces an algorithm to identify wildlife species using image recognition, facilitating streamlined monitoring processes. Manual identification proves challenging with a diverse range of species, making automated classification essential. The application of robust deep learning algorithms enables effective wildlife recognition and classification, aiding in the prevention of wildlife-vehicle collisions, tracking of animals, and mitigation of poaching activities. By leveraging a dataset of wildlife images, the algorithm learns to categorize animals accurately, contributing to the preservation of biodiversity. Moreover, the proposed approach demonstrates promising results in classification accuracy, suggesting its potential to enhance existing wildlife conservation practices. The integration of such technological advancements into conservation strategies offers a scalable and efficient solution to address challenges in wildlife management. This research underscores the importance of employing innovative technologies to safeguard vulnerable species and their habitats in the face of increasing human-wildlife interactions and environmental threats.