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Data · dataset · 2025

UAV-Acquired Dataset for Farm Intrusion Detection

Listed in Teesside University Research Data Repository

Description This dataset was developed at Soroti University (Uganda) to support machine learning research in UAV-based farm security.

Description

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.

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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.

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Provenance · 1 source records, 13 field assertions
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