As wild pigs (Sus scrofa) expand throughout North America researchers are increasingly being tasked with trapping and marking entire sounders (family groups) to attach monitoring devices or other identifying markers to gather knowledge to inform management. Capture and marking procedures can be challenging, dangerous for both researchers and animals, and time consuming, particularly when handling sounders. We developed an integrated pig‐handling system to efficiently sort, weigh, chemically immobilize, and mark multiple wild pigs simultaneously in a controlled manner. To assess the functionality of the system, we evaluated 18 capture events in Texas, USA, from January 2018 to March 2019, where we marked 221 pigs of varied age classes and group sizes (2–19 animals). Using the pig‐handling system, we chemically immobilized 51 large (41–101 kg) pigs and manually restrained 170 smaller (<45 kg) pigs with injury rates below 4%. Average handling times for large pigs was 71.9 (SD = 25.7) min and <1 min for smaller ones. We released sounders intact and routinely recorded them together on motion‐activated cameras. Incorporating a handling system into wild pig research and management is encouraged to facilitate safe handling procedures for both pigs and handlers. © 2021 The Wildlife Society. This article is a U.S. Government work and is in the public domain in the USA.

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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,

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