Detection of Litchi Leaf Diseases and Insect Pests Based on Improved FCOS

Abstract

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model’s generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases’ and pests’ prevention and control.