Quality recognition method of oyster based on U-net and random forest

doi:10.1016/j.jfca.2023.105746

IOCAS-IR > 实验海洋生物学重点实验室

	Quality recognition method of oyster based on U-net and random forest
	Zhao, Feng 1; Hao, Jinyu 1; Zhang, Huanjia 1; Yu, Xiaoning 1; Yan, Zhenzhen 1; Wu, Fucun 2,3,4,5
	2024
发表期刊	JOURNAL OF FOOD COMPOSITION AND ANALYSIS
ISSN	0889-1575
卷号	125 页码:9
通讯作者	Wu, Fucun([email protected])
摘要	Oysters are one of the most important cultivated marine resources globally. The shape of oysters is an essential reference criterion for consumers to judge the quality of oysters. In order to recognize oyster's shape, the U-Net model and random forest are combined to compose a creative strategy. To be more specific, the U-Net neural network model is firstly developed to segment the image and obtain the contours of oysters, and the shape features of oysters are extracted. Then, a random forest model with shape feature parameters depending on customer preference is created to identify oyster quality. The results indicate that the intersection-over-union of segmentation outcomes achieved by U-Net reaches 99.06%, surpassing the 93.50% obtained by traditional methods. The accuracy of the classification strategy based on the shape features parameters of consumer preference is 94.18%, which further proves the effectiveness of the proposed strategy. This study might provide valuable data and guidelines to oyster product classification based on shell shape within market contexts.
关键词	Oyster Quality recognition U-Net Random forest Consumer preference
DOI	10.1016/j.jfca.2023.105746
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA 24030105]; National Natural Science Foundation of China[62176140]; National Natural Science Foundation of China[82001775]; National Natural Science Foundation of China[61972235]; National Natural Science Foundation of China[61976124]; National Natural Science Foundation of China[61873117]; National Natural Science Foundation of China[61976125]; Key Research and Development Program of Shandong[2022LZGC015]; Key Research and Development Program of Shandong[ZFJH202309]
WOS研究方向	Chemistry ; Food Science & Technology
WOS类目	Chemistry, Applied ; Food Science & Technology
WOS记录号	WOS:001098748800001
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
WOS关键词	IMAGE ; LINE
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/183862
专题	实验海洋生物学重点实验室
通讯作者	Wu, Fucun
作者单位	1.Shandong Technol & Business Univ, Sch Comp Sci & Technol, Yantai, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Ctr Ocean Mega Sci, CAS & Shandong Prov Key Lab Expt Marine Biol, Qingdao 266071, Peoples R China 3.Pilot Natl Lab Marine Sci & Technol, Lab Marine Biol & Biotechnol, Qingdao 266237, Peoples R China 4.Natl & Local Joint Engn Lab Ecol Mariculture, Qingdao 266071, Peoples R China 5.Shandong Technol Innovat Ctr Oyster Seed Ind, Qingdao, Peoples R China
通讯作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Zhao, Feng,Hao, Jinyu,Zhang, Huanjia,et al. Quality recognition method of oyster based on U-net and random forest[J]. JOURNAL OF FOOD COMPOSITION AND ANALYSIS,2024,125:9.
APA	Zhao, Feng,Hao, Jinyu,Zhang, Huanjia,Yu, Xiaoning,Yan, Zhenzhen,&Wu, Fucun.(2024).Quality recognition method of oyster based on U-net and random forest.JOURNAL OF FOOD COMPOSITION AND ANALYSIS,125,9.
MLA	Zhao, Feng,et al."Quality recognition method of oyster based on U-net and random forest".JOURNAL OF FOOD COMPOSITION AND ANALYSIS 125(2024):9.