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

This paper presents a semi-automatic method for detecting damage to corn crops using RGB images acquired with a UAV, fully integrated with the QGIS environment. The method uses vegetation indices (ExG, GLI, MGRVI) and unsupervised k-means clustering, with interactive tuning of the results using a dedicated QGIS plug-in. The proposed approach enables rapid, repeatable and low-cost assessment of wildlife damage without the need for multispectral sensors or artificial intelligence. The method can be used operationally by non-specialists without GIS or coding skills, making it ideal for farmers, field technicians and local environmental managers.

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