Quality recognition method of oyster based on U-net and random forest
文献类型：期刊论文
| 作者 | Zhao, Feng4; Hao, Jinyu4; Zhang, Huanjia4; Yu, Xiaoning4; Yan, Zhenzhen4; Wu, Fucun1,2,3,5 |
| 刊名 | JOURNAL OF FOOD COMPOSITION AND ANALYSIS
 |
| 出版日期 | 2024 |
| 卷号 | 125页码:9 |
| 关键词 | Oyster Quality recognition U-Net Random forest Consumer preference |
| ISSN号 | 0889-1575 |
| DOI | 10.1016/j.jfca.2023.105746 |
| 通讯作者 | Wu, Fucun(wufucun@qdio.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | IMAGE ; LINE |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:001098748800001 |
| 出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/183861]  |
| 专题 | 海洋研究所_实验海洋生物学重点实验室 |
| 通讯作者 | Wu, Fucun |
| 作者单位 | 1.Pilot Natl Lab Marine Sci & Technol, Lab Marine Biol & Biotechnol, Qingdao 266237, Peoples R China 2.Shandong Technol Innovat Ctr Oyster Seed Ind, Qingdao, Peoples R China 3.Natl & Local Joint Engn Lab Ecol Mariculture, Qingdao 266071, Peoples R China 4.Shandong Technol & Business Univ, Sch Comp Sci & Technol, Yantai, Peoples R China 5.Chinese Acad Sci, Inst Oceanol, Ctr Ocean Mega Sci, CAS & Shandong Prov Key Lab Expt Marine Biol, Qingdao 266071, Peoples R China |
| 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. |
入库方式： OAI收割
来源：海洋研究所
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