In the current aquaculture industry, the detection of physiological stress indicators in fish during waterless transportation is considered to be an effective method to monitor their health status. However, commonly used detection methods are invasive and interfere with the accuracy of detection results. In order to solve this problem, this study takes grouper as an example, combines multi-sensor physiological stress indicators monitoring, and designs a health status assessment and classification model based on image features and deep learning models, which inputs real-time fish image feature information data acquired during low-temperature waterless transportation into the optimised CNN-BiGRU classification model based on whale optimisation algorithm (WOA) proposed in this study, so as to achieve an accurate assessment of the health status of the fish. By inputting the image feature datasets based on time series and classified into the WOA-CNN-BiGRU, BiGRU, and GRU classification models for training, validation, and testing, respectively, the effectiveness of the classification performance of the models was verified by using accuracy, precision, recall and F1-value composite scores as the metrics. It was found that the WOA-CNN-BiGRU model had excellent classification performance, followed by the BiGRU and GRU models, especially in classifying the health status assessment of small size grouper. This study proposes an efficient and inexpensive method for real-time monitoring and accurate assessment of the health status of fish during waterless transportation, and also provides a reference for vigour monitoring and health assessment of other similar aquatic products.
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