Multiple regression and artificial neural network for the prediction of crop pest risks

Abstract

The reduction of crop yield losses caused by pests is a major challenge to productive and sustainable food production for preventing food insecurity and emergencies, and for alleviating world food crisis. Multiple regression (MR) and artificial neural network (ANN) are two widely adopted modelling approaches for the prediction of crop pest risks, which are based on empirical statistics and artificial intelligence, respectively. Each of the two alternative approaches has its advantages and disadvantages. This study evaluates the two models from two aspects: their performances on pest risk prediction, and their methodological advantages and disadvantages. Two pest species are modelled using the two approaches as case studies, which are the melon thrip Thrips palmi Karny (T. palmi) and the diamondback moth Plutella xylostella (L.) (P. xylostella). Results show that ANN has higher prediction accuracy for both species. However, ANN has some methodological demerits compared to MR modelling.