To solve the problem that the detection effect of crop pests and diseases is not ideal due to the complicated image background and the interference of irrelevant factors, this paper proposes a novel crop pests and diseases detection based on fuzzy neural network and multilevel feature fusion in remote sensing images. Firstly, the model is based on YOLOv5 and extracts the semantic level information of different depth features from the convolutional neural network, and then combines the weight aggregation module to learn the weight of each layer feature adaptively. Then the learned weights are loaded to the segmentation graphs obtained by sampling on each feature layer to obtain the final segmentation results. In this model, a fuzzy learning module is added to the skip connection part to remove noise features and alleviate the uncertainty between classes. The traditional cross entropy loss involves activating the output value with the Softmax function and calculating a weighted cross entropy loss with the label. If the weight of the cross entropy loss term is not adjusted, the model will tend to update the weight related to the background, which makes it difficult to deal with the category imbalance in remote sensing images. Therefore, we use focus loss to alleviate the problem of class imbalance in images. The results on public data sets show that the accuracy rate of the proposed model in this paper is over 95%, the recall rate is over 85%, and the average accuracy is 91.2%. In terms of F1, compared with other advanced methods, the presented method has achieved improvements of 6.6%, 8.5%, and 7.7% respectively. It shows that the new model has strong robustness and generalization for crop pest detection.
Yin et al. (Thu,) studied this question.