Detection of Citrus Psyllid Based on Improved YOLOX Model

Abstract

【Objective】Yellow-shoot disease, known as the cancer of citrus, is a devastating disease, and psyllid is the main vector of yellow-shoot disease transmission, therefore, monitoring and precise disinfection and sterilization of psyllid is an effective way to prevent and control yellow-shoot disease and inhibit its transmission.【Method】The traditional way to eliminate the psyllid was mainly to spray drugs manually, and the control effect was not ideal due to high labor costs. In the study, we used an improved YOLOX based edge detection method for psyllid, added Convolutional Block Attention Module (CBAM) to the backbone network, and further extracted important features in the channel and space dimensions. The cross entropy loss in the target loss was changed to Focal Loss to further reduce the missed detection rate.【Result】The results showed that the algorithm described in the study fitted in with the detection platform of psyllid. The data set of psyllid was taken in Lianjiang Orange Garden, Zhanjiang City, Guangdong Province. It is deeply adapted to the actual needs of agricultural and rural development. Based on YOLOX model, the backbone network and loss function were improved to achieve a more excellent detection method of citrus psyllid. 85.66% of the AP value was obtained on the data set of citrus psyllid, which was 2.70 percentage points higher than that of the original model, and the detection accuracy was 8.61, 4.32 and 3.62 percentage points higher than that of YOLOv3, YOLOv4-Tiny and YOLOv5-s respectively, which has been greatly improved.【Conclusion】The improved YOLOX model can better identify citrus psyllid, and the accuracy rate has been improved, laying a foundation for the subsequent real-time detection platform.

Keywords

• citrus
• improved yolox model
• prevention and control of psyllid
• artificial intelligence
• object detection