Wildlife conservation is a growing challenge due to habitat loss, illegal poaching, climate change, and the lack of continuous monitoring systems in remote forest areas. Traditional methods of wildlife tracking, such as manual observation and field surveys, are often slow, inaccurate, and unable to provide real-time information. These limitations make it difficult for authorities to detect threats early, monitor animal movements, and take timely action.To address these challenges, this project proposes the development of a YOLOv8-Based Wildlife Species and Poacher Detection System, an automated solution that uses advanced computer vision techniques for efficient wildlife monitoring.The system uses an Image Detection Engine powered by YOLOv8 to process images and video feeds captured from camera traps and drones. It identifies different wildlife species and detects unauthorized human presence. The collected data is analyzed to recognize patterns in animal movement and behavior, enabling continuous and non-intrusive monitoring.For advanced analysis, the system uses deep learning models to improve detection accuracy even in complex environments such as low light, dense forests, or occluded views. It can also identify potential threats like poachers and generate instant alerts for quick response.Furthermore, the system includes a Monitoring and Alert Module and Decision Support Logic to provide real-time notifications, species information, and actionable insights for forest authorities. These features help in reducing human–wildlife conflict and improving conservation strategies.All system op-erations are supported through a User Interface and Data Management Framework, allowing researchers and officials to access data, upload images, and generate reports easily. This ensures a scalable, efficient, and intelligent platform for wildlife monitoring, protection, and sustainable ecosystem management.

This study presents a deep learning-driven framework for detecting wildlife species and identifying potential threats such as poachers by analyzing image and video data. The system utilizes visual features such as object shape, size, texture, and patterns captured from camera traps and drone feeds to evaluate wildlife presence and human intrusion.The architecture of the system is organized into multiple layers, including the user interface, processing module, and data storage component. Users can upload images or monitor live feeds through the interface, while the processing layer handles data validation, preprocessing, feature extraction, and detection using the YOLOv8 model. The outcomes are then displayed and stored for future reference.The selection of the YOLOv8 algorithm is based on its ability to perform real-time object detection with high accuracy and efficiency. Additionally, the system incorporates an alert generation mechanism, which enhances the practical usability and responsiveness of the system in real-world scenarios.Experimental results indicate that the proposed approach performs well in detecting wildlife species and identifying unauthorized human presence. By analyzing multiple visual factors simultaneously, the system can detect potential threats at an early stage and support timely intervention. Beyond detection, the system assists forest au-thorities by providing actionable insights for effective wildlife monitoring and conservation.

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