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Prediction method of large yellow croaker (Larimichthys crocea) freshness based on improved residual neural network

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Abstract

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 °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.

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Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was financially supported by Major Scientific and Technological Innovation Project of Shandong Province (2022CXGC020414), and the Key Research and Development Program of Shandong Province (2021SFGC0701).

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Authors and Affiliations

  1. College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, 256300, China

    Xudong Wu, Zongmin Wang, Qingxiang Zhang, Hongbo Yan & Lanlan Zhu

  2. School of Computer Science and Technology, Shandong University of Technology, Zibo, 256300, China

    Zhiqiang Wang & Qing Zhang

  3. Jing Hai Group Co., LD, Rongcheng, 264300, China

    Jie Chang

  4. Sanqi Biomedicine (Shandong) Co., LTD, Rizhao, 276800, China

    Daixin Liu

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Contributions

XW: Methodology, Software, Writing—original draft preparation. ZW: Validation, Methodology, Writing—review and editing. ZW: Methodology, Writing—review and editing. QZ: Validation, Software. QZ: Conception, Validation. HY: Validation. LZ: Methodology, Writing—review and editing, Supervision, Funding acquisition. JC: Investigation, Source. DL: Investigation, Source. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Lanlan Zhu.

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Wu, X., Wang, Z., Wang, Z. et al. Prediction method of large yellow croaker (Larimichthys crocea) freshness based on improved residual neural network. Food Measure 18, 2995–3007 (2024). https://doi.org/10.1007/s11694-024-02381-5

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