CNN - Enabled Wildlife Surveillance and Monitoring System

This study uses convolutional neural networks (CNNs) to automate wildlife surveillance by classifying animal species from images. It starts with collecting and preprocessing image data through resizing, normalization, augmentation, and noise reduction. Transfer learning with models like VGG16 or ResNet is used to enhance accuracy and training efficiency. A CNN model is developed using convolutional, pooling, and fully connected layers. The model is evaluated using accuracy, precision, recall, and F1-score. Challenges like lighting variation, background clutter, and occlusion are addressed using optimization techniques. The system supports conservation efforts and suggests future improvements with real-time monitoring and object detection.