Intelligent Wildlife Activity Monitoring and Alert Generation with Hybrid DNN

Wildlife monitoring plays a vital role in protecting biodiversity and preventing threats such as poaching, habitat destruction, and human–animal conflicts. Traditional methods of tracking animal activity are mostly manual, time-consuming, and prone to errors, making them less effective in addressing these challenges. To overcome these limitations, we propose an intelligent wildlife activity monitoring and alert generation system based on a Hybrid Deep Neural Network (DNN). The system collects data from multiple sources such as camera traps, acoustic sensors, and motion detectors, and processes it using a hybrid model that integrates Convolutional Neural Networks (CNNs) for image and video recognition with Long Short-Term Memory (LSTM) networks for temporal behavior analysis. This combination enhances the system’s ability to accurately identify animal species, detect unusual activities, and predict potential risks. Once abnormal or critical behavior is recognized, the system automatically generates alerts that are transmitted to forest officials through mobile or IoT platforms for immediate action. By reducing human effort, improving detection accuracy, and enabling real-time decision-making, the proposed system contributes significantly to wildlife conservation, ecosystem research, and the prevention of human–wildlife conflicts.