Citrus leaf disease detection based on improved YOLO11 with C3K2

Citrus planting plays an important role in China's agricultural planting. It is not only a characteristic industry with remarkable economic benefits, but also an important way to help rural revitalization and increase farmers' income. China has the largest citrus planting area in the world, but its output ranks only second, among which pest and disease infection is one of the main reasons leading to the decline of citrus output. Aiming at the problem of citrus leaf disease detection, this paper proposes a C3K2E module combining multi-scale edge information enhancement module, which is embedded into the backbone feature extraction network of YOLOv11 to optimize its disease detection ability. By improving the module design, the model not only enhances the ability to identify diseased areas, but also enhances the ability to capture small targets and edge features, achieving more efficient and accurate disease detection. The test results on the citrus leaf disease data set show that the map accuracy of the method reaches 0.952, which is 3 percentage points higher than 0.922 of the basic model YOLOv11n, significantly verifying the superiority of the proposed method.