Detection of citrus pests in double backbone network based on single shot multibox detector

Abstract

To prevent and control agricultural diseases and insect pests, the timely detection and accurate identification of crop diseases and insect pests are significant. Studies have shown that pests on plant surfaces are challenging to detect because of their small size and strong camouflage. Therefore, to better detect pests on citrus leaves, a citrus disease and insect pest detection method based on a double backbone network is proposed. The double backbone network-improved Single Shot MultiBox Detector (SSD) model was used to detect citrus images. The accuracy and recall rate of the neural network target detection were evaluated, and the robustness was verified by analyzing the detection results. The experimental results showed that the trained network’s mean average precision (mAP) on the test dataset was 72.54%. In addition, the model showed high robustness on citrus pest datasets, with mAP reaching 86.01%. The results showed that the method was accurate and efficient compared with other target detection methods and could be applied to detect and control citrus pests and diseases.