VISION-BASED AI MODELS FOR WILDLIFE CONSERVATION AND SPECIES TRACKING

The accelerating loss of biodiversity due to habitat destruction, climate change, and poachingnecessitates the development of advanced conservation tools. Traditional wildlife monitoring methods—manualtracking, field surveys, and camera traps—are often labor-intensive, costly, and geographically limited. VisionbasedArtificial Intelligence (AI) models have emerged as transformative technologies capable of automatingand scaling wildlife observation through image and video data. Powered by deep learning and computer vision,these models can perform species recognition, behavioral analysis, population estimation, and illegal activitydetection with unprecedented speed and accuracy. This paper explores the foundations of vision-based AI inwildlife conservation, including key algorithms and data sources. It details various use cases such as speciesidentification, habitat monitoring, and poacher detection. Real-world applications from regions including Africa,South America, and Southeast Asia demonstrate how these models are revolutionizing conservation strategies.The paper also discusses ethical and regulatory considerations related to data usage, community involvement,and potential surveillance misuse. Technical challenges such as data quality, model bias, and deployment inremote areas are evaluated. Finally, future innovations such as edge AI, explainable models, and citizen scienceintegration are explored. Vision-based AI holds immense potential to support biodiversity preservation andempower conservationists with precise, scalable, and non-invasive monitoring tools.