SE-VisionTransformer: Hybrid Network for Diagnosing Sugarcane Leaf Diseases Based on Attention Mechanism

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-10, Vol.23 (20), p.8529
• Main Authors: Sun, Cuimin, Zhou, Xingzhi, Zhang, Menghua, Qin, An
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Algorithms; Business metrics; Classification; Computational linguistics; convolutional neural network; Datasets; Deep learning; Innovations; Language processing; Machine learning; multi-head self-attention; Natural language interfaces; Neural networks; Plant diseases; SE attention; Sugarcane; sugarcane disease; Support vector machines
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23208529

Sugarcane is an important raw material for sugar and chemical production. However, in recent years, various sugarcane diseases have emerged, severely impacting the national economy. To address the issue of identifying diseases in sugarcane leaf sections, this paper proposes the SE-VIT hybrid network. Unlike traditional methods that directly use models for classification, this paper compares threshold, K-means, and support vector machine (SVM) algorithms for extracting leaf lesions from images. Due to SVM’s ability to accurately segment these lesions, it is ultimately selected for the task. The paper introduces the SE attention module into ResNet-18 (CNN), enhancing the learning of inter-channel weights. After the pooling layer, multi-head self-attention (MHSA) is incorporated. Finally, with the inclusion of 2D relative positional encoding, the accuracy is improved by 5.1%, precision by 3.23%, and recall by 5.17%. The SE-VIT hybrid network model achieves an accuracy of 97.26% on the PlantVillage dataset. Additionally, when compared to four existing classical neural network models, SE-VIT demonstrates significantly higher accuracy and precision, reaching 89.57% accuracy. Therefore, the method proposed in this paper can provide technical support for intelligent management of sugarcane plantations and offer insights for addressing plant diseases with limited datasets.