Technical Feasibility Analysis of a Low-Cost UAV-Based Computer Vision System for Wildlife Monitoring
jeevanr17 · Aug 15, 2026
Abstract. Wildlife monitoring in forested and environmentally challenging regions requires observation systems that can provide sufficient coverage while reducing the dependence on continuous manual surveillance. Unmanned aerial vehicles (UAVs) combined with computer vision provide a potential approach for automated wildlife observation; however, practical deployment is constrained by factors such as payload capacity, propulsion requirements, wireless communication, computational resources, environmental conditions, and detection performance. This paper presents a technical feasibility analysis of a low-cost UAV-based wildlife monitoring system integrating a Pixhawk 2.4.8 flight controller, GPS-assisted navigation, an ESP32-CAM for image acquisition, Wi-Fi-based video transmission, and YOLOv8s for animal detection. The detection model was trained for ten wildlife classes using a dataset containing 5,553 images and 8,014 annotated object instances, with 4,442 images used for training and 1,111 for validation. The model was trained for 100 epochs at an image size of 640 × 640 pixels with a batch size of 8. At the final training epoch, the model achieved a validation precision of 94.01%, recall of 94.01%, mAP@50 of 96.76%, and mAP@50–95 of 83.54%. The system was also examined at the system level with respect to wireless communication, image acquisition, and environmental conditions. The UAV configuration uses A2212/13T 1000-KV brushless motors, 10 × 4.5 propellers, and an 11.1-V 5400-mAh Li-Po battery, with a UAV mass of approximately 2.0 kg and a considered payload range of 1.2–1.5 kg. Engineering calculations are used to examine the propulsion requirements for the resulting take-off mass, while flight endurance is treated as a theoretical parameter because an experimentally measured endurance value was not established. The analysis evaluates the technical feasibility of integrating low-cost UAV hardware with external computer-vision processing while identifying communication dependence, low-light degradation, and the absence of onboard AI processing as important limitations.