Lightweight citrus leaf disease detection model based on ARMS and cross-domain dynamic attention

Abstract

In citrus cultivation, Anthracnose, Scab, and Greasy Spot significantly impact yield and quality. Facing challenges in detecting small targets against complex orchard backgrounds with uneven lighting and obstructions, existing models suffer from low detection accuracy. This study introduces the YOLOv8n-CDDA citrus leaf disease detection model. The Cross-Domain Dynamic Attention (CDDA) mechanism deconstructs the backbone network’s input feature maps into sections, dynamically assigning spatial and channel attention weights to reconstruct critical information and capture the variations and weak semantic features of disease textures. The proposed Adaptive Random Mix-Cut Splicing (ARMS) image augmentation technique blends diseased leaf images with healthy citrus backgrounds, enhancing the diversity and number of background targets. To reduce computational and memory consumption, the network is streamlined through channel pruning; to compensate for the loss in accuracy from pruning, a teacher–assistant–student network format is used for knowledge distillation, where the student network learns from soft knowledge to improve disease recognition accuracy. Finally, Grad-CAM++ technology generates heatmaps of the detections, facilitating the visualization of effective features and deepening understanding of the model’s focus areas. Experimental results demonstrate that the YOLOv8n-CDDA model achieves an average accuracy of 90.89% in disease detection, with an average recall rate of 81.12%, and a mean Average Precision (mAP50) of 88.36%. Compared to the original YOLO v8n and current mainstream detection models such as YOLOv5s, SSD, and Faster-RCNN, the improvements in average accuracy are respectively 2.95%, 4.78%, 14.22%, and 21.01%; in average recall, 2.36%, 3.09%, 15.74%, and 23.27%; and in mAP50, 2.38%, 3.13%, 13.45%, and 20.91%. After pruning and distillation for lightweight adaptation, the YOLOv8n-CDDA model has a parameter size of 0.8M, requires 4.2 GFLOPs, weighs 2.0 MB, and operates at 45 fps. Compared to YOLOv8n, this represents a reduction of 2.2M in parameters, 3.9 GFLOPs, and 4 MB in model weight, with an increase of 7 fps in speed. This model exhibits exceptional performance in the complex environment of citrus leaf disease detection, providing robust technical support for citrus growth monitoring studies, and offering insights for disease detection in other crops as well.