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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23208529