A Semi-Automated RGB-Based Method for Wildlife Crop Damage Detection Using QGIS-Integrated UAV Workflow

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 15; p. 4734
• Main Authors: Banaszek, Sebastian, Szota, Michał
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31.07.2025; MDPI
• Subjects: Adaptation; Agricultural production; Agriculture - methods; Algorithms; Animals; Animals, Wild; Automation; Calibration; Classification; Corn; Crop damage; Crops, Agricultural; Data processing; Design; Heuristic; Image Processing, Computer-Assisted - methods; Methods; Performance evaluation; Python; QGIS; Remote Sensing Technology - methods; RGB; Sensors; UAV; Unmanned Aerial Devices; Unmanned aerial vehicles; Usability; Vegetation; vegetation indices; Workflow; Zea mays; UAV; crop damage; RGB; QGIS; vegetation indices
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25154734

Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management.