UAV-Based Surveillance of Endangered Wildlife Using Machine Learning and Artificial Intelligence

The Unmanned Aerial Vehicles (UAVs), together with Machine Learning (ML) and Artificial Intelligence (AI), are the emerging technologies for the transformation of wildlife conservation. EcoDroneNet is introduced in this paper as a novel framework integrating drone surveillance with advanced AI analytics to detect, classify, and protect endangered wildlife. The system utilizes high-resolution aerial imagery and sensor data, analyzed through Convolutional Neural Networks (CNNs) and bioacoustics algorithms, to realize real-time species detection, behavior tracking, and threat identification. The present framework achieves a near 98 percent accuracy when implemented for population estimation, habitat mapping, and robber detection. Thus, the study argues for using UAVs in data collection in inaccessible terrains and AI to gain actionable insights through pattern recognition, thermal and light detection and ranging (LiDAR) analyses, and predictive modeling. Furthermore, considerations have been made regarding environmental ethics, including airspace regulations, data protection, and factors concerning wildlife disturbance. EcoDroneNet is a scalable, non-intrusive, low-cost biodiversity monitoring solution that merges new technology with eco-goals. Ultimately, the work aims to give a strong motivation for proactive conservation measures and the formulation of sustainable protection operations for wildlife.