Abstract Assessment and intelligent monitoring of fish freshness are of the utmost importance in yield and trade of fishery products. Rapid and precise assessment of fish freshness using conventional methods considering the great volume of industrial production is challenging. In this study, instead of feature-engineering-based methods, a novel and accurate fish freshness detection is proposed based on the images obtained from common carp and by applying a deep convolutional neural network (CNN). To classify fish images based on freshness by the proposed approach, first, VGG-16 architecture was applied to extract features from fish images automatically. Then, a developed classifier block constructed by dropout and dense layers was utilized to classify fish images. The obtained results showed the classification accuracy of 98.21%, and in conclusion, the proposed CNN-based method has lower complexity with higher accuracy compared to traditional classification methods. This method is well-capable of monitoring and classifying fish freshness as a fast, low-cost, precise, non-destructive, real-time and automated technique.

中文翻译：

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基于智能深度学习的鲤鱼无损新鲜度诊断方法

摘要 鱼类新鲜度的评估和智能监测在渔业产品的产量和贸易中至关重要。考虑到大量的工业生产，使用传统方法快速准确地评估鱼的新鲜度具有挑战性。在这项研究中，基于从鲤鱼获得的图像并应用深度卷积神经网络 (CNN)，提出了一种新颖且准确的鱼类新鲜度检测，而不是基于特征工程的方法。为了通过所提出的方法基于新鲜度对鱼类图像进行分类，首先，应用 VGG-16 架构自动从鱼类图像中提取特征。然后，利用由 dropout 和密集层构建的开发分类器块对鱼类图像进行分类。得到的结果表明分类准确率为 98.21%，总而言之，与传统分类方法相比，所提出的基于 CNN 的方法具有更低的复杂性和更高的准确度。该方法是一种快速、低成本、精确、无损、实时和自动化的技术，能够很好地监测和分类鱼类的新鲜度。