Evaluation of Maize Crop Damage Using UAV-Based RGB and Multispectral Imagery

The accurate evaluation of crop damage by wild animals is crucial for farmers when seeking compensation from insurance companies or other institutions. One the game species that frequently cause in Europe boar, which often feeds on maize. Other species, such as roe deer and red deer, can also significant damage. This study aimed to assess accuracy based remote sensing data derived unmanned aerial vehicles (UAVs), especially a digital surface model (DSM) RGB imagery NDVI (normalized difference vegetation index) multispectral imagery, at two growth stages During first stage, plants are intensive phase green, was conducted using both DSM NDVI. Each variable separately utilized, variables were included classification regression tree (CART) analysis, wherein categorized binomial (with without damage). In second before harvest had dried, only employed evaluation. results demonstrated high detecting areas with damage, but this primarily observed larger than several square meters. significantly lower smaller very narrow areas, width single maize row. proved be more useful it applied any stage growth.