Using drones to detect and quantify wild pig damage and yield loss in corn fields throughout plant growth stages

Abstract

Presently, there are an estimated 6.9 million wild pigs (Sus scrofa) in the U.S., which cause over US$1 billion in damage to agriculture, environmental impacts, and control costs. However, estimates of damage have varied widely, creating a need for standardized monitoring and a method to accurately estimate the economic costs of direct wild pig damage to agriculture. The goal of our study was to integrate remotely sensed imagery from drones and crop harvest data to quantify wild pig damage in corn fields. We used drones with natural color (red, green, blue) cameras to monitor corn fields at different growth stages in an agricultural matrix in Delta County, Texas, USA, during 2019-2020. We flew 36 drone missions and classified wild pig damage in 18 orthomosaics by a combination of manually digitizing and deep-learning algorithms. We compared estimates of damage from drone imagery to those derived from ground-based transect surveys, to verify pig damage. Finally, we compared damaged areas of fields to maps of collected real-time yields at harvest to estimate yield loss. All classified drone orthomosaics of pig damage had >80% overall accuracy for all growth stages. Ground transect surveys,