Classification of apple leaf diseases based on MobileViT transfer learning

doi:10.1117/12.3035225

	Classification of apple leaf diseases based on MobileViT transfer learning
	Ding, Yongjun; Yang, Wentao
	2024
会议名称	2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024
会议录名称	Proceedings of SPIE - The International Society for Optical Engineering
卷号	13213
会议日期	April 19, 2024 - April 21, 2024
会议地点	Suzhou, China
会议录编者/会议主办者	Academic Exchange Information Centre (AEIC) ; Ajeenkya DY Patil University
出版者	SPIE
摘要	In recent years, the apple growing industry in Qingyang has been seriously threatened by many diseases such as grey mould, rust, brown spot, scar disease and leaf spot. These diseases have caused significant losses to the local economy. This study discusses the method of combining computer technology with deep learning to accurately diagnose these diseases, with the aim of reducing their negative impact on agricultural development. To this end, we collected early disease data sets of apple leaves in Ningxian Modern Agricultural Industrial Park in Qingyang City, Gansu Province, and Haisheng Apple Planting Base in Yulinzi Town, Zhengning County. Considering the problems of low accuracy of classification of apple leaf diseases, difficulty in collecting data sets and huge model parameters, this paper selects grey spot, rust, brown spot, scarring and leaf spot as the research objects, and proposes a MobileViT model suitable for small sample size based on the theory of deep transfer learning. The model aims to solve the problems of large model, low precision and small sample in the process of apple leaf disease detection under complex background. Firstly, MobileViT, Vision Transformer and Swin Transformer are used for the training of the model transfer learning. The experimental results show that the accuracy rate of the MobileViT model is 97.3%, the loss value is 0.169, the model size is 18.9 MB, and the prediction time of a single image is only 2.6 ms. Furthermore, the MobileViT model is optimised by freezing different training strategies, the migration strategy is the most effective, so the average accuracy of the model in apple leaf disease classification reaches 98.54%, and the loss value drops to 0.125. Finally, we developed a WeChat applet to deploy the trained model, and realised the visualisation of apple leaf disease classification. This innovative application not only improves the efficiency and accuracy of disease classification, but also provides new opportunities for the modernisation and intelligence of agricultural technology. © 2024 SPIE.
关键词	Agricultural technology Classification (of information) Fruits Attention mechanisms Brown spots Data set Disease classification Leaf disease Leaf spots Lightweight Mobilevit Small samples Transfer learning
DOI	10.1117/12.3035225
收录类别	EI
语种	英语
EI入藏号	20243216828279
EI主题词	Deep learning
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 716.1 Information Theory and Signal Processing ; 821.4 Agricultural Products ; 903.1 Information Sources and Analysis
原始文献类型	Conference article (CA)
EISSN	1996-756X
ISSN	0277-786X
文献类型	会议论文
条目标识符	https://ir.nwnu.edu.cn/handle/39RV6HYL/98794
专题	实体学院_计算机科学与工程学院
通讯作者	Ding, Yongjun
作者单位	College of Computer Science & Engineering, Northwest Normal University, Lanzhou; 730030, China
第一作者单位	计算机科学与工程学院
通讯作者单位	计算机科学与工程学院
第一作者的第一单位	计算机科学与工程学院
推荐引用方式 GB/T 7714	Ding, Yongjun,Yang, Wentao. Classification of apple leaf diseases based on MobileViT transfer learning[C]//Academic Exchange Information Centre (AEIC), Ajeenkya DY Patil University:SPIE,2024.