Risk mapping of redheaded cockchafer ('Adoryphorus couloni') (Burmeister) infestations using a combination of novel k-means clustering and on-the-go plant and soil sensing technologies

Abstract

The ability to identify areas of pasture that are more likely to support damaging levels of the soil-borne, redheaded cockchafer ('Adoryphorus couloni') (Burmeister) (RHC) would allow farmers to target expensive control measures. This study explored soil properties, measured via electromagnetic surveys (EM38), pasture biomass via active optical sensors (CropCircle™) and topography via GPS elevation survey as potential indicators of RHC population density. A combination of these variables was used to produce risk maps with an accuracy of 88% at predicting likely RHC density-categories on a dairy property in the Gippsland region of Victoria, Australia. This risk mapping protocol could be used to improve sampling programs and direct site-specific pest management.