During rice cultivation, common rice diseases and pests such as Rice blast, Bacterial blight, Brown-planthopper and Leaf-folder will significantly affect the yield and quality. The current model is limited to detecting rice diseases or pests alone, and faces challenges such as the diversity of disease and pest traits, small detection targets, uneven light and complex background shading in paddy fields, resulting in low accuracy and adaptability of the model. In this study, a YOLO-DP (Diseases and Pests) model based on YOLOv8n model was proposed to detect fifteen common rice diseases and pests under complex conditions. First, the Triplet Attention mechanism is introduced into the network's Backbone to achieve cross-dimensional interaction between channels and spatial dimensions. Then, GLSA (the Global to Local Spatial Aggregation) module is used to improve the Neck of YOLOv8n, enhancing the effectiveness of feature representation. The WTConv (Wavelet Transform Convolution) is used to improve the C2f-BottleNeck of the original model, expanding the network's receptive field. Finally, the loss function is replaced with EIoU (Enhanced Intersection over Union) to reduce the position offset and shape mismatch of the predicted boxes. Experimental results demonstrate that the model achieves an average accuracy of 80.9%, a recall rate of 74.4%, a Mean Average Precision mAP50 of 77.8% and mAP95 of 50.1%, significantly outperforming the original YOLOv8n and mainstream detection models such as TOOD, Faster R-CNN and RT-DETR. This model exhibits exceptional performance in detecting rice diseases and pests in complex environments, providing robust technical support for rice growth monitoring and offering insights for the detection of other crop diseases.
Zhou et al. (Wed,) studied this question.