Channel grouping vision transformer for lightweight fruit and vegetable recognition

doi:10.1016/j.eswa.2025.128636

	Channel grouping vision transformer for lightweight fruit and vegetable recognition
	Liu, Chengxu 1; Min, Weiqing 2,3; Song, Jingru 1; Ang, Yancun Y.1; Sheng, Guorui 1; Yao, Tao 1; Wang, Lili 1; Jiang, Shuqiang 2,3
	2025-11-01
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	292 页码:11
摘要	Recognizing fruit and vegetable is crucial for improving processing efficiency, automating harvesting, and facilitating dietary nutrition management. The diverse applications of fruit and vegetable recognition require deployment on end devices with limited resources, such as memory and computing power. The key challenge lies in designing lightweight recognition algorithms. However, current lightweight methods still rely on simple CNN-based networks, which fail to deeply explore and specifically analyze the unique features of fruit and vegetable images, resulting in unsatisfactory recognition performance. To address this challenge, we propose a novel lightweight recognition network termed Channel Grouping Vision Transformer (CGViT). CGViT utilizes a channel grouping mechanism and half-convolution to enhance feature extraction capability while reducing complexity. This design enables the model to capture three discriminative types of features from images. Subsequently, the Transformer is employed for feature fusion and global information extraction, ultimately creating an efficient neural network model for fruit and vegetable recognition. The proposed CGViT approach achieved recognition accuracies of 71.26%, 99.99%, 98.92 %, and 61.33 % on four fruit and vegetable datasets, respectively, outperforming state-of-the-art methods (MobileViTV2, MixNet, MobileNetV2). The maximum memory usage during training is only 6.48GB, which is merely 13.8% of that required by state-of-the-art methods(MobileViTv2). The fruit and vegetable recognition model proposed in this study offers a more profound and effective solution, providing valuable insights for future research and practical applications in this domain. The code is available at https://github.com/Axboexx/CGViT.
关键词	Fruit recognition Vegetable recognition Lightweight Deep learning Computer vision
DOI	10.1016/j.eswa.2025.128636
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:001519016100004
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42296
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Sheng, Guorui
作者单位	1.Ludong Univ, Sch Informat & Elect Engn, Yantai 264025, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Liu, Chengxu,Min, Weiqing,Song, Jingru,et al. Channel grouping vision transformer for lightweight fruit and vegetable recognition[J]. EXPERT SYSTEMS WITH APPLICATIONS,2025,292:11.
APA	Liu, Chengxu.,Min, Weiqing.,Song, Jingru.,Ang, Yancun Y..,Sheng, Guorui.,...&Jiang, Shuqiang.(2025).Channel grouping vision transformer for lightweight fruit and vegetable recognition.EXPERT SYSTEMS WITH APPLICATIONS,292,11.
MLA	Liu, Chengxu,et al."Channel grouping vision transformer for lightweight fruit and vegetable recognition".EXPERT SYSTEMS WITH APPLICATIONS 292(2025):11.