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Article

Multi-Scale and Multi-Factor ViT Attention Model for Classification and Detection of Pest and Disease in Agriculture

by
Mingyao Xie
and
Ning Ye
*
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
*
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 / Revised: 1 July 2024 / Accepted: 2 July 2024 / 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.
Keywords: pest and disease in agriculture; data augmentation; classification; object detection; multi-scale; ViT pest and disease in agriculture; data augmentation; classification; object detection; multi-scale; ViT

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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