Prevention of Animal Poaching Using Convolutional Neural Network-Based Approach

This research study focuses on the prevention of animal poaching through the use of camera traps, machine learning technology, and mathematical algorithms. It elucidates a comparison between the various available machine learning (neural network) models available for image recognition and highlights the one better suited for the mechanism’s environment and intended use. The proposed methodology detects motion in the images obtained from a camera trap and focuses only on the moving area for classification. Following this, the algorithm detects the presence of humans in the area, and accordingly, the study highlights a mathematical algorithm to be used to devise patrolling routes in order to apprehend or catch poachers. The study also highlights the challenges related to power efficiency, limited Internet connectivity, and potential risks to the security of the system. The proposed system aims to help tackle the problem of animal poaching and protect our wildlife.