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In Flanders (northern Belgium), wild boar (Sus scrofa) returned in 2006 after 50 years of absence and the population is increasing, both in abundance and geographic extent. In the absence of wild boar, Flanders' landscape structure changed into a dense, mosaic‐like pattern of agricultural, natural, and urban areas. The return of the wild boar increasingly leads to human–wildlife conflicts, mainly linked to damage in agriculture. Hence, there is a growing need for a time‐efficient, standardized, and accurate method to assess crop damage. We present an Unmanned Aerial Vehicle‐based method, using Geographic Object‐Based Image Analysis and Random Forests to estimate the damaged area and associated yield losses, between 2015 and 2017, due to wild boar in individual fields in Flanders. Our approach resulted in an 84.50% overall accuracy in calculating damaged area for maize fields and 94.40% for grasslands. Damage levels ranged between 14.3% and 20.2% in maize fields and 16.5% to 25.4% in grasslands. Our method can provide objective base data for compensation schemes and guide management strategies based on damage assessments. © 2018 The Wildlife Society. We used drones, Geographic Object‐Based Image Analysis and Random Forest models to assess crop damage by wild boar. [ABSTRACT FROM AUTHOR]