【Objective】 Accurate detection is the key to precise control of plant diseases and pests. Building an accurate and efficient monitoring model of plant diseases and pests provides an important basis for the early diagnosis and warning of plant diseases and pests.【Method】In view of the weak generalization ability of the existing plant diseases and pests detection models and the high rate of missed detection of small targets, a plant disease and pest detection model based on visual attention enhancement improvement YOLOv 5-VE (vision enhancement) was proposed. The Mosaic 9 data enhancement method was used to facilitate the detection of small targets in experimental samples; a feature enhancement module based on a visual attention convolutional block attention module (CBAM) was designed; to determine the location loss of overlapping and occluded targets, the DIoU bounding box location loss function was introduced.【Result】The recognition and average detection accuracies of the YOLOv 5-VE model on the experimental dataset reached 65.87% and 73.49%, respectively, which were 1.07% and 8.25% higher, respectively, than those of the original model. The detection speed on the GPU with the model 1 080 Ti reached 35 fps. 【Conclusion】This method can quickly and effectively detect and identify a variety of diseases and pests in field scenes with complex backgrounds, improve robustness of detection, improve the feature extraction ability of the model for pests and diseases, reduce the interference of complex field scenes in detection, and shows good application potential. It can be widely used for the large-scale detection of plant diseases and pests.