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Open AccessArticle
Multi-Scale and Multi-Factor ViT Attention Model for Classification and Detection of Pest and Disease in Agriculture
by
Mingyao Xie
Mingyao Xie
Mingyao Xie received a B.S. degree from Nanjing Forestry University, Nanjing, China, in 2021 and now [...]
Mingyao Xie received a B.S. degree from Nanjing Forestry University, Nanjing, China, in 2021 and now she continues to pursue an M. S. degree in Computer Technology. Her current work is on making improvements on classification and detection of pests and diseases in agricultural and forestry through Artificial Intelligence.
and
Ning Ye
Ning Ye
Ning Ye is a professor in the College of information Science and Technology & Artificial at Nanjing [...]
Ning Ye is a professor in the College of information Science and Technology & Artificial Intelligence at Nanjing Forestry University, Nanjing, China. His main research areas are bioinformatics, data mining, machine learning, 3D reconstruction, and algorithm complexity analysis. Meanwhile, he is also a director of the Computer Branch of the Chinese Forestry Society, a senior member of the Chinese Computer Society, and a director of the Jiangsu Provincial Computer Society.
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College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(13), 5797; https://doi.org/10.3390/app14135797 (registering DOI)
Submission received: 9 May 2024
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Revised: 1 July 2024
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Accepted: 2 July 2024
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Published: 2 July 2024
Abstract
Agriculture has a crucial impact on the economic, ecological, and social development of the world. More rapid and precise prevention and control work, especially for accurate classification and detection, is required due to the increasing severity of agricultural pests and diseases. However, the results of the image classification and detection are unsatisfactory because of the limitation of image data volume acquisition and the wide range of influencing factors of pests and diseases. In order to solve these problems, the vision transformer (ViT) model is improved, and a multi-scale and multi-factor ViT attention model (SFA-ViT) is proposed in this paper. Data augmentation considering multiple influencing factors is implemented in SFA-ViT to mitigate the impact of insufficient experimental data. Meanwhile, SFA-ViT optimizes the ViT model from a multi-scale perspective, and encourages the model to understand more features, from fine-grained to coarse-grained, during the classification task. Further, the detection model based on the self-attention mechanism of the multi-scale ViT is constructed to achieve the accurate localization of the pest and disease. Finally, experimental validation of the model, based on the IP102 and Plant Village dataset, is carried out. The results indicate that the various components of SFA-ViT effectively enhance the final classification and detection outcomes, and our model outperforms the current models significantly.
Share and Cite
MDPI and ACS Style
Xie, M.; Ye, N.
Multi-Scale and Multi-Factor ViT Attention Model for Classification and Detection of Pest and Disease in Agriculture. Appl. Sci. 2024, 14, 5797.
https://doi.org/10.3390/app14135797
AMA Style
Xie M, Ye N.
Multi-Scale and Multi-Factor ViT Attention Model for Classification and Detection of Pest and Disease in Agriculture. Applied Sciences. 2024; 14(13):5797.
https://doi.org/10.3390/app14135797
Chicago/Turabian Style
Xie, Mingyao, and Ning Ye.
2024. "Multi-Scale and Multi-Factor ViT Attention Model for Classification and Detection of Pest and Disease in Agriculture" Applied Sciences 14, no. 13: 5797.
https://doi.org/10.3390/app14135797
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