Detection of Laodelphax striatellus (small brown planthopper) based on improved YOLOv5

Abstract

Laodelphax striatellus (small brown planthopper, SBPH) is a pest feeding mainly on rice, wheat and corn. Meanwhile, SBPH transmits plant viruses during feeding, which may take more serious damage to the agriculture. The research of SBPH control has great significance. Most of the traditional SBPH detection and counting problems are conducted manually, which is inefficient. In this paper, we proposed the improved YOLOv5s algorithm to implement the recognition and counting the system of SBPH. We improve the YOLOv5s based on the channel attention and spatial attention, adding the Convolutional Block Attention Module (CBAM) to YOLOv5s. In order to fuse the feature information of different scales to improve the model, we add the Adaptively Spatial Feature Fusion (ASFF). Experiments prove that the improved model volume is not much different from YOLOv5s, which is one-seventh of YOLOv5x, but the mAP of the improved model is approximately 4 % better than YOLOv5s and 2 % better than YOLOv5x. Finally, an APP was developed using Android Studio. We developed image recognition, counting module, real-time recognition and counting module, which were migrated to the mobile phone.