Deep learning based soybean seed classification

doi:10.1016/j.compag.2022.107393

	Deep learning based soybean seed classification
	Huang, Ziliang 1,3; Wang, Rujing 1,3; Cao, Ying 4; Zheng, Shijian 2; Teng, Yue 1,3; Wang, Fenmei 1,3; Wang, Liusan 3; Du, Jianming 3
刊名	COMPUTERS AND ELECTRONICS IN AGRICULTURE
	2022-11-01
卷号	202
关键词	Attention mechanism Image classification Image segmentation Lightweight convolutional neural networks Soybean seed
ISSN号	0168-1699
DOI	10.1016/j.compag.2022.107393
通讯作者	Wang, Liusan(lswang@iim.ac.cn) ; Du, Jianming(djming@iim.ac.cn)
英文摘要	Accurately sorting high-quality soybean seeds is a crucial and time-consuming task in quality inspection and food safety. This paper designs a full pipeline to classify the soybean seeds, which follows a segmentation- classification procedure. The image segmentation is performed by a popular deep learning method, the Mask R-CNN, while the classification stage is performed through a novel network, named Soybean Network (SNet). SNet is an extremely lightweight model based on convolutional networks, and it contains mixed feature recalibration (MFR) modules. The MFR module is designed to improve the representation ability of our SNet for damage features so that the model pays more attention to the key regions. Experimental results show that the proposed SNet model achieves 96.2% identification accuracy with only 1.29M parameters, which outperforms six previous state-of-the-art models. The proposed SNet could be used for the automatic recognition of soybean seeds on the resource-limited platform.
资助项目	National Key Research and Devel-opment Program of China ; National Natural Science Foundation of China ; [2018YFD0101004] ; [31671586]
WOS研究方向	Agriculture ; Computer Science
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000868905900003
资助机构	National Key Research and Devel-opment Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/129869]
专题	中国科学院合肥物质科学研究院
通讯作者	Wang, Liusan; Du, Jianming
作者单位	1.Univ Sci & Technol China, Hefei 230026, Peoples R China 2.Southwest Univ Sci & Technol, Mianyang 621010, Peoples R China 3.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 4.Gansu Vocat Coll Architecture, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Huang, Ziliang,Wang, Rujing,Cao, Ying,et al. Deep learning based soybean seed classification[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2022,202.
APA	Huang, Ziliang.,Wang, Rujing.,Cao, Ying.,Zheng, Shijian.,Teng, Yue.,...&Du, Jianming.(2022).Deep learning based soybean seed classification.COMPUTERS AND ELECTRONICS IN AGRICULTURE,202.
MLA	Huang, Ziliang,et al."Deep learning based soybean seed classification".COMPUTERS AND ELECTRONICS IN AGRICULTURE 202(2022).