Detection of Soybean Insect Pest and a Forecasting Platform Using Deep Learning with Unmanned Ground Vehicles

Abstract

Soybeans (Glycine max (L.) Merr.), a popular food resource worldwide, have various uses throughout the industry, from everyday foods and health functional foods to cosmetics. Soybeans are vulnerable to pests such as stink bugs, beetles, mites, and moths, which reduce yields. Riptortus pedestris (R. pedestris) has been reported to cause damage to pods and leaves throughout the soybean growing season. In this study, an experiment was conducted to detect R. pedestris according to three different environmental conditions (pod filling stage, maturity stage, artificial cage) by developing a surveillance platform based on an unmanned ground vehicle (UGV) GoPro CAM. Deep learning technology (MRCNN, YOLOv3, Detectron2)-based models used in this experiment can be quickly challenged (i.e., built with lightweight parameter) immediately through a web application. The image dataset was distributed by random selection for training, validation, and testing and then preprocessed by labeling the image for annotation. The deep learning model localized and classified the R. pedestris individuals through a bounding box and masking in the image data. The model achieved high performances, at 0.952, 0.716, and 0.873, respectively, represented through the calculated means of average precision (mAP) value. The manufactured model will enable the identification of R. pedestris in the field and can be an effective tool for insect forecasting in the early stage of pest outbreaks in crop production.