Image detection model construction of Apolygus lucorum and Empoasca spp. based on improved YOLOv5

Abstract

AbstractBACKGROUNDThe polyphagous mirid bug Apolygus lucorum (Meyer‐Dür) and the green leafhopper Empoasca spp. Walsh are small pests that are widely distributed and important pests of many economically important crops, especially kiwis. Conventional monitoring methods are expensive, laborious and error‐prone. Currently, deep learning methods are ineffective at recognizing them. This study proposes a new deep‐learning‐based YOLOv5s_HSSE model to automatically detect and count them on sticky card traps.RESULTSBased on a database of 1502 images, all images were collected from kiwi orchards at multiple locations and times. We trained the YOLOv5s model to detect and count them and then changed the activation function to Hard swish in YOLOv5s, introduced the SIoU Loss function, and added the squeeze‐and‐excitation attention mechanism to form a new YOLOv5s_HSSE model. Mean average precision of this model in the test dataset was 95.9%, the recall rate was 93.9% and the frames per second was 155, which are higher than those of other single‐stage deep‐learning models, such as SSD, YOLOv3 and YOLOv4.CONCLUSIONThe proposed YOLOv5s_HSSE model can be used to identify and count A. lucorum and Empoasca spp., and it is a new, efficient and accurate monitoring method. Pest detection will benefit from the broader applications of deep learning. © 2024 Society of Chemical Industry.