Intelligent Animal Detection for Smart Agricultural Management Using AI-Based Solutions

The project titled "Wild Animals Detection Using YOLO Model" focuses on the development of a robust computer vision system to identify and classify wild animals in real-time. The system leverages the advanced object detection capabilities of the YOLO (You Only Look Once) model to enhance accuracy and efficiency. This project is designed to address challenges in wildlife monitoring, conservation efforts, and human-wildlife conflict mitigation. The YOLO model processes images and videos, detecting multiple animal species simultaneously with high speed and precision. Using a pre-trained YOLO model, the system is fine-tuned on a dataset of diverse wild animal images to ensure its adaptability to various environments and lighting conditions. The solution emphasizes scalability, enabling integration with drones and surveillance systems for real-world applications.The system's ability to detect and localize animals in real-time aids wildlife researchers, park rangers, and authorities in tracking animal movement, preventing poaching, and responding to potential threats. By automating the detection process, this project minimizes human intervention and errors, promoting safety and conservation. In conclusion, this innovative application of YOLO technology serves as a significant step towards using AI in wildlife monitoring and ecosystem protection, ensuring sustainable coexistence between humans and nature. 

    Keywords: Wild Animals Detection, YOLO Model, Real-time Detection, Computer Vision, Object Detection.