外文摘要：Conventional evaluation of fish freshness based on physiological and biochemical methods was destructive, complicated and costly. In this study, the new model was trained on the eye images of 100 large yellow croakers along with their total volatile basic nitrogen (TVB-N) value as freshness indicators in the storage of nine consecutive days at 4 degrees C. The experiment was divided into three stages (0-2 days, 3-6 days, and 7-8 days) based on TVB-N value, about 1000 images in each stage were used for freshness classification. A non-destructive and rapid fish freshness detection method based on the eye region images of large yellow croaker was proposed by mathematical modeling. The features of large yellow croaker images were extracted automatically by ResNet-34 structure, and then the key extracted feature was focused on the pupil of the fish eye by mixed attention mechanism. Finally, the features of pupil were used to classify the freshness of large yellow croaker. The results showed the accuracy of the model to classify the fish freshness was reached to 99.4%. The model constructed based on the eye images was non-destructive, and could well monitor and distinguish the freshness of large yellow croakers at different storage stages.
外文关键词：deep learning;Convolutional Neural Network;attention mechanism;Non-destructive detection;Fish freshness;Residual neural network
作者：Wang, Zhiqiang;Wu, Xudong;Wang, Zongmin;Zhang, Qing;Zhang, Qingxiang;Yan, Hongbo;Zhu, Lanlan;Chang, Jie;Liu, Daixin
作者单位：Shandong Univ Technol;Jing Hai Grp Co Ltd;Sanqi Biomed Shandong Co Ltd
期刊名称：JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION
期刊影响因子：0.0
出版年份：2024
出版刊次：18（4）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台