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

Abstract

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