Abstract:

To improve the detection accuracy of citrus leaf edge diseases and small-target lesions by models and enhance the performance of existing detection models, an efficient and lightweight citrus leaf disease detection model named YOLOv8-DTBI is proposed based on the baseline model YOLOv8n. Firstly, a more lightweight C2f_DT module is introduced into the backbone of the baseline model. This module adopts a combined structure of dual convolution and triplet attention to strengthen the model’s feature extraction capability. Secondly, a bidirectional feature pyramid network (BiFPN) is integrated into the baseline model, and a small-target detection layer is constructed. This not only reduces the number of model parameters but also improves the model’s ability to detect small targets of citrus leaf diseases. Finally, the model is trained based on the Inner IoU loss function to accelerate bounding box regression and enhance the model’s precision and recall. Experimental results show that the proposed YOLOv8-DTBI model demonstrates better detection performance on the citrus leaf disease dataset, as the precision, recall, and mean average precision (mAP) of the proposed YOLOv8-DTBI model reach 89.2%, 90.8% and 92.1%, respectively, which are 5.6, 5.3, and 1.4 percentage points higher than those of the YOLOv8n model, and the model size is reduced by 8.5%. This study provides a practical detection model for the accurate detection of citrus leaf diseases.

Key words: citrus leaf disease, dual convolution, triplet attention, machine vision, model optimization