{"title":"Quality non-destructive sorting of large yellow croaker based on image recognition","authors":"","doi":"10.1016/j.jfoodeng.2024.112227","DOIUrl":null,"url":null,"abstract":"<div><p>The YOLOv7 algorithm was used to establish a fast and non-destructive quality identification model for large yellow croaker (<em>Larimichthys crocea</em>) in this study. The cross multi-head attention mechanism in the swin transformer was incorporated into the neck of YOLOv7 architecture to enhance the recognition performance. The freshness classification model based on the total volatile basic nitrogen value was evaluated by freshness indicators, visual features, and texture profile analysis (TPA). The findings indicated that the enhanced model achieved an accuracy rate of 98.6% in freshness classification, which was higher than 85.6% of the original model. Visual features (fish-eye plumpness and turbidity) were highly correlated with all freshness indexes (all above 0.8). The accuracy of freshness discrimination in different lighting environments was also greater than 90%. These results collectively indicate the potential for the eye region images to serve as a reliable indicator for the sorting of freshness in large yellow croakers.</p></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0260877424002930","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The YOLOv7 algorithm was used to establish a fast and non-destructive quality identification model for large yellow croaker (Larimichthys crocea) in this study. The cross multi-head attention mechanism in the swin transformer was incorporated into the neck of YOLOv7 architecture to enhance the recognition performance. The freshness classification model based on the total volatile basic nitrogen value was evaluated by freshness indicators, visual features, and texture profile analysis (TPA). The findings indicated that the enhanced model achieved an accuracy rate of 98.6% in freshness classification, which was higher than 85.6% of the original model. Visual features (fish-eye plumpness and turbidity) were highly correlated with all freshness indexes (all above 0.8). The accuracy of freshness discrimination in different lighting environments was also greater than 90%. These results collectively indicate the potential for the eye region images to serve as a reliable indicator for the sorting of freshness in large yellow croakers.
期刊介绍:
The journal publishes original research and review papers on any subject at the interface between food and engineering, particularly those of relevance to industry, including:
Engineering properties of foods, food physics and physical chemistry; processing, measurement, control, packaging, storage and distribution; engineering aspects of the design and production of novel foods and of food service and catering; design and operation of food processes, plant and equipment; economics of food engineering, including the economics of alternative processes.
Accounts of food engineering achievements are of particular value.