DEEP LEARNING BASED SNAKE INTRUSION MONITORING SYSTEM

Snake interactions have the potential to compromise ecological diversity and public safety in both human andanimal habitats. There is interest in a snake detection and alert system that uses AI and ML technologies as apossible remedy for this issue. By accurately identifying snakes and promptly issuing notifications, the deviceseeks to reduce human-snake confrontations and foster stronger bonds. Real-time video processing, automateddecision making, and object recognition methods like YOLO are how it does this. Recent studies and projects inthis field have demonstrated that machine learning techniques can recognize snakes in video data. Deep learningmethods have been used in several research; for example, YOLO enables you to detect snakes rapidly andcorrectly. To gain a better understanding of how contemporary systems adjust to various situations, further studyis required. With the aid of these goals, the project aims to provide a complete and practical solution that willcontribute to the safety of people and wildlife in areas where snakes are common. One of computer vision'smain tasks is object detection. Complicated models for machine learning may currently be utilized toautomatically discover and categorize items from potentially complicated photos because to the tremendousadvancements in machine learning throughout the last 20 years, particularly deep learning. This researchexamines the implementation and performance of machine (deep) suitable learning networks for snake detectionand classification in a mobile setting alert.