Wildlife Monitoring

Wildlife monitoring is increasingly reliant on artificial intelligence to automate the analysis of vast datasets from various sources, including camera traps and other sensors, primarily aiming to improve species identification, assess biodiversity, and combat poaching. Current research focuses on applying and refining deep learning models, such as convolutional neural networks (CNNs) including YOLOv8, ResNet, and DenseNet, often incorporating metadata to enhance accuracy and efficiency. These advancements offer significant potential for improving conservation efforts by providing more timely and accurate information on species populations, behavior, and threats, ultimately aiding in more effective management strategies.