UAV-Acquired Dataset for Farm Intrusion Detection

DescriptionThis dataset was developed at Soroti University (Uganda) to support machine learning research in UAV-based farm security. It contains 2,067 RGB images organized into three categories:Animals (907 images) – livestock such as goats and cows, captured under varying poses and lighting conditions.People (588 images) – potential human intruders recorded primarily from UAV aerial perspectives.Empty Spaces (572 images) – unoccupied farmland areas (grass, soil, fences) included as a negative class to reduce false positives.Key features:Acquired using a DJI Mavic 3 Cine UAV and supplemented with smartphone ground captures.Images resized to 255×255 pixels for computational efficiency while retaining distinguishing features.Collected across diverse altitudes, times of day, and environmental conditions.Accompanied by baseline benchmarking results using CNN and MobileNetV3 Small classifiers (96.8% and 98.4% accuracy, respectively).Ethically curated: personally identifiable features (faces, license plates) were blurred or removed; data collected with landowner consent.This dataset represents the first publicly available UAV-acquired intrusion detection dataset tailored to agricultural security. It enables benchmarking, reproducibility, and development of AI-driven solutions for livestock farm protection.