A Classification Method for Maize Leaf Disease Based on SimCLR and Triplet Attention

Abstract

Highlights Improving the robustness of maize leaf disease classification models in complex environments using multiple data enhancement methods. Fusing ResNet50 with the triplet attention mechanism to improve feature extraction. Reducing the use of labeled data to some extent by using SimCLR. The classification accuracy of four different leaf types was 91.87%, which was higher than other compared models. Abstract. Deep learning methods for classifying maize leaf diseases often need a lot of labeled data for training during the model training phase, but labeling data is sometimes difficult and expensive. In addition, complicated environmental circumstances can readily impede the recognition effect. In order to solve this issue, SimCLR and the triplet attention mechanism were coupled to create a system for classifying maize leaf diseases. Firstly, to increase the robustness of the model, several data enhancement techniques were used to imitate the interference of complex components in the natural environment. Secondly, the use of labeled data was minimized by learning the similarity between related categories using a self-supervised SimCLR framework. The feature extraction network simultaneously employed a triplet attention mechanism to enhance the model’s ability to extract information about the interactions between space and channel, improve attention to discriminative features, and lessen interference from complex factors. In order to verify the effects of incorporating SimCLR and triplet attention mechanisms, this work used three different types of maize leaf diseases and healthy leaves as experimental objects. The final enhanced model was compared and analyzed with seven widely used image classification models. The experimental results demonstrated that the maize leaf disease classification model suggested in this article effectively enhanced the classification performance of maize leaf diseases containing complex factors of interference with the test set, with the network model proposed in this article having an average classification accuracy of 91.87%, better than the seven comparative models and requiring no additional labeled data during the training process. Keywords: Image classification, Maize leaf disease, Self-supervised learning, SimCLR, Triplet attention module.