Aquatic products are often required to assess their quality and safety during fish processing. However, conventional chemical assays and sensory evaluation cannot fully meet the ever-increasing needs in modern aquaculture. Among them, chemical analysis is usually destructive, labor-intensive, and low-speed for rapid or large-scale inspection. While the sensory evaluation can depend heavily on subjective experience and judgment, leading to its reliability in quality assessment. Alternatively, hyperspectral imaging (HSI) has emerged as an attractive nondestructive approach to evaluate fish quality. The imaging can be expected to integrate with spectroscopy, thereby simultaneously capturing spatial and spectral information from fish muscle. The visible, near infrared, and short-wave infrared regions can be covered over the broad range of quality variations in fish tissues. Physicochemical changes include the freshness, moisture distribution, lipid characteristics, and microbial spoilage. Spectral signals can provide the spatial distribution to evaluate multiple quality attributes without damage to the sample. The HSI technique can also share the superiority to conventional imaging and destructive analytical methods in fish-quality detection. In this review, the recent progress was systematically summarized in the application of HSI into fish freshness, moisture, fat, microbial spoilage, and parasite contamination. Much emphasis was also placed on the spectral ranges most frequently in previous research. The approaches were selected based on the informative wavelengths, modeling, and their predictive performance. Meanwhile, some attention was also given to the main barriers to the broader use of HSI. Among them, it was lacking in consistency in spectral acquisition and instrument standards, data processing and interpretation, adaptability to different sample-handling conditions and real application scenarios, multi-index fusion and model generalization, equipment costs, and unified standards. Taken together, it was also required to translate laboratory findings into industrial practice. Looking ahead, future advances were likely to involve closer equipment integration, higher levels of intelligence, multimodal data fusion, closed-loop process control, and deeper application in fish-quality evaluation. At the same time, the standardized technical frameworks and more complete industrial systems are also essential for wider application. Furthermore, the HSI can be integrated with the sensing technologies and intelligent decision-making tools for high detection efficiency, robustness, and practical applicability. Overall, this finding can offer a useful reference to advance the intelligent upgrading of fish-product processing for the high quality and safety of aquatic products.

Overfishing has been one of the greatest risks to marine biodiversity in the world in recent years. Thus, the production of many catches is in a yearly decline, including Acetes chinensis. At the same time, an Acetes chinensis quota has been introduced to promise the marine conservation of biodiversity in China in 2020. Accurate and rapid quantification of fishing vessel fishing information can be one of the most important prerequisites to implementing the fine management of quota fishing. This study aims to perform the target identification and statistics quantification of Acetes chinensis quota fishing. An electronic monitoring (EM) device was installed on Acetes chinensis quota fishing vessels to monitor the main operation process of fishing vessels. An improved target detection algorithm (YOLOv7-MO) and target counting algorithm (YOLOv7-MO-SORT) using YOLOv7 were proposed to realize the target detection and statistics of Acetes chinensis quota fishing vessels. The YOLOv7-MO target detection algorithm used the MobileOne as the backbone network. The C3 modules were added to the head part of the output during the pruning operation. The YOLOv7-MO-SORT target counting algorithm was selected to replace the Faster R-CNN from the SORT (Simple Online and Realtime Tracking) algorithm replaced by YOLOv7-MO for the detection of anchors thrown during fishing operations and baskets containing Acetes chinensis. Kalman filtering and Hungarian matching algorithms were used to track and predict the detected targets, according to the characteristics of actual production operations. The collision detection lines, timestamps, thresholds, and counters were set to count the number of baskets of Acetes chinensis caught and nets during the fishing operation. The results show: (1) The improved YOLOv7-MO was achieved in average detection accuracy, recall, and F1 score of 97.3%, 96.0%, and 96.6%, respectively, on the test set, which were improved by 2.0, 1.1 and 1.5 percentage points, compared with the original model. (2) The improved YOLOv7-MO model size, the number of parameters, and the number of floating-point operations were 64.0 MB, 32.6 M, and 39.7 G, respectively, which were 10.2%, 10.6%, and 61.6% smaller than those of the YOLOv7 model. (3) The accuracy of the SORT algorithm Acetes chinensis fishing operation count with YOLOv7-MO as the detector reached 80.0% and 95.8% in counting the number of Acetes chinensis baskets and the number of nets, respectively. YOLOv7-MO reduced the model magnitude while improving the detection accuracy and efficiency. The SORT algorithm with the YOLOv7-MO as the detection head was also achieved in more accurate statistical quantification of the main operational information of fishing vessels. This function can be expected to facilitate the management and recording of fishing vessel operations, in order to avoid some drawbacks of the traditional manual recording of fishing vessel operations. The gross shrimp basket count statistics can provide better convenience to calculate the fishing parameters, such as the gross shrimp CPUE. The identification can be realized to count the fishing vessel operation of hairy shrimp fishing. The finding can also provide a strong reference to realize the automation and intelligence of recording fishing operations in offshore vessels, particularly for the decision-making on the hairy shrimp quota fishing.

In recent years, with the rapid development and expansion of the global aquaculture industry, and the continuous enlargement of aquaculture farms, the industrialization, intelligence, and informatization of aquaculture have become a trend in the industry. China has become the largest producer of fisheries and aquaculture. Fish farming is an important component of aquaculture, and fish farming monitoring has become an important technology to enhance the efficiency, production, and management of fish farming. Fish farming monitoring can provide real-time and accurate data for farms, assisting farm managers in making decisions to improve efficiency and production. With the emergence of artificial intelligence technology in recent years, deep learning has rapidly developed and been widely applied in various fields such as image and audio recognition, natural language processing, robotics, bioinformatics, chemistry, and finance. The monitoring of fish farming focuses on the quantity, growth, behavior, and health status of fish. Using deep learning technology, we can quickly and accurately obtain information related to fish farming and enhance its efficiency and management. This paper presents a deep learning-based method for fish farming monitoring and reviews the literature progress in fish length measurement, fish counting, fish feeding, fish swimming behavior, and fish disease diagnosis. Although deep learning-based fish length measurement has achieved high accuracy in underwater environments, some errors still exist. The counting methods based on deep learning can be categorized into segmentation counting, detection counting, tracking counting, and density regression counting. Deep learning models based on video data have higher accuracy in recognizing fish feeding behavior than image-based models. There have been many studies on fish tracking, but practical applications still face challenges such as fish feature extraction, the influence of fish size and obstructions, and occlusion issues. In fish disease diagnosis, it is necessary to establish standardized and shared fish disease datasets and utilize data fusion, data level information fusion, feature level information fusion, and decision level information fusion. This article also summarizes the main problems of deep learning-based visual technologies in fish farming monitoring from the aspects of monitoring data acquisition and transmission, dataset standardization and processing, deep learning model design, and the lack of business application in fish farming intelligent monitoring. The problems in data acquisition include a limited variety of experimental subjects, a small number of samples, and poor performance of experimental equipment. In the data transmission process, there are challenges in data security and real-time transmission. In terms of datasets, there is a low level of standardization and a lack of large-scale unified datasets. There is also a lack of research on large models and embedded models in deep learning model design. Furthermore, there is a realistic problem of insufficient business application in practical settings. The paper also proposes future research directions, including establishing fish farming monitoring datasets, super-scale parameter models for fish farming, edge computing for terminal monitoring devices, and digital twinning in fish farming monitoring, aiming to provide scientific references for the widespread application of deep learning in fish farming monitoring.