Abstract: In order to achieve rapid and accurate detection of cotton diseases in real field environments,this paper proposes a cotton disease target detection model based on feature enhancement and attention mechanism.To ensure the accuracy of the model’sdetection in real field environments,an improved feature enhancement module is used in the Neck module to weight the feature maps and reduce the interference of background or other objects on the targets in the image.After the feature enhancement mo-dule,Group Mix Attention is used to connect contextual information and enrich the feature map information.The proposed model can effectively improve the detection accuracy of models in real field environments,effectively reducing the occurrence of model false positives and false negatives using SIoU loss function.The experimental results show that the proposed model performs well on the self built real field environment cotton disease target detection dataset,effectively improving the detection accuracy of the model in real field environments.Compared with the baseline model,the mAP and Precision have increased by 2 percentage points and 4.5 percentage points.

Key words: Cotton diseases and pests, Small goals, Feature enhancement, Attention mechanism, Loss function