Quantifying damage from wild pigs with small unmanned aerial systems

Abstract

Wild pig (Sus scrofa) population expansion and associated damage to crops, wildlife, and the environment is a growing concern in the United States. The destructive rooting behavior of wild pigs indicates where they have foraged and their general presence on the landscape. We used aerial imagery with a small unmanned aerial system to assess damage of corn (Zea mays) fields by wild pigs in the Mississippi Alluvial Valley of Mississippi, USA, during the 2016 growing season. Images were automatically classified using segmentation-based fractal texture analysis and support vector machines. We assessed the accuracy of automated classification with 5,400 Global Positioning System ground reference points collected in the fields. Classification accuracies for identification of damaged and nondamaged areas were between 65% and 78%. In general, automated classification underestimated the area of damage present within fields. Kappa values ranged from 0.26 to 0.51, on a scale of 0.0-1.0. Small unmanned aerial systems overcome limitations of existing methods because they can survey an entire field rapidly and without significant field labor. Ó 2018 The Wildlife Society.

Use of unmanned aircraft systems (UAS) and multispectral imagery for quantifying agricultural areas damaged by wild pigs

“…Estimates of area damaged by wild pigs (0.005-0.2 ha) were similar to estimates in Mississippi using UAS and segmentation-based fractal texture analysis (Samiappan et al, 2018), but considerably less than estimates in northern Belgium using UAS and geographic object-based image analysis (Rutten et al, 2018). This may be the result of wild pig elimination programs occurring across Missouri and much the U.S., which has presumably lowered wild pig densities (Centner and Shuman, 2015;U.S.…”

“…Although inaccuracies in classification maps and estimated damage areas will exist for any UAS-based approach, we consider our method to be an objective, time-efficient, and accurate approach. Past studies, using visible or crop height information, have indicated that UAS-based imagery approaches underestimate damage caused by wild pigs (Michez et al, 2016;Samiappan et al, 2018). Some of this error was due to ortho-mosaic and classification procedures, while other error was due to alignment errors of ground reference data and damage maps.…”

“…daily or even hourly revisit times), thus they can assist with precision agriculture (Candiago et al, 2015;Christiansen et al, 2017;Hunt and Daughtry, 2018), rangeland monitoring (Laliberte et al, 2010;Laliberte et al, 2011;Sankey et al, 2018), forest mapping (Wallace et al, 2012;Getzin et al, 2014;Birdal et al, 2017), ecosystem monitoring (Habel et al, 2018), invasive species mapping (Samiappan et al, 2017), and detecting and monitoring pest infestations (Lehmann et al, 2015;Puig et al, 2015). Data collected by UAS have also been used recently to estimate damage to agricultural crops by wild pigs (Michez et al, 2016;Rutten et al, 2018;Samiappan et al, 2018). In particular, Michez et al (2016) used a crop height model and manual ortho-photo delineation to estimate damage to corn.…”

“…Kuzelka and Surovy (2018) used a similar crop height approach to delineate wild pig damage to wheat. Samiappan et al (2018) used visible information and textural analysis classifiers to delineate damaged areas in corn fields.…”

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

“…The recent proliferation and expansion of wild pigs is credited to both behavioral and anthropogenic factors (Seward et al 2004, Bevins et al 2014, Tabak et al 2017). Wild pigs are highly adaptable, have an omnivorous diet, and high reproductive rates (Singer et al 1984, Samiappan et al 2018). Besides dispersing on their own, humans also translocate and release wild pigs due to their popularity with hunters (Seward et al 2004, Bevins et al 2014, Tabak et al 2017, McCann et al 2018, Fischer et al 2020).…”