The effect of cutting and fish‐orientation systems on the deheading yield of carp

Dowgiałło, Andrzej

doi:10.1111/j.1365-2621.2008.01750.x

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…Furthermore, the fish cutting route was typically along a straight line to simplify the cutting process. 39 However, identifying the curves and profile boundaries of the target would be the optimal strategy to maximize the meat yield and avoid the protein waste. Five state-of-the-art deep learning models were thus selected for further analysis and comparison based on their performances of catfish semantic segmentation, including BEiTV1, 40 SegFormer-B0 and SegFormer-B5, 41 ViT-Adapter, 42 and PSPNet 43 with the resized/unified input image size of 640x640 across all models (Table 3).…”

Section: Deep Learning Models For Semantic Segmentationmentioning

confidence: 99%

Automating catfish cutting process using deep learning-based semantic segmentation

Thayananthan

Zhang

Liu

et al. 2023

Sensing for Agriculture and Food Quality and Safety XV

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Mississippi and Alabama are the top two states producing and processing catfish in the United States, with the annual production of $382 million in 2022. The catfish industry supplies protein-rich catfish products to the U.S. market and contributes considerably to the development of the local economy. However, the traditional catfish processing heavily relies on human labors leading to a high demand of workforce in the processing facilities. De-heading, gutting, portioning, filleting, skinning, and trimming are the main steps of the catfish processing, which normally require blade-based cutting device (e.g., metal blades) to handle. The blade-based manual catfish processing might lead to product contamination, considerable fish meat waste, and low yield of catfish fillet depending on the workers’ skill levels. Furthermore, operating the cutting devices may expose the human labors to undesired work accidents. Therefore, automated catfish cutting process appears to be an alternative and promising solution with minimal involvement of human labors. To further enable, assist, and automate the catfish cutting technique in near real-time, this study presents a novel computer vision-based sensing system for segmenting the catfish into different target parts using deep learning and semantic segmentation. In this study, 396 raw and augmented catfish images were used to train, validate, and test five state-of-the-art deep learning semantic segmentation models, including BEiTV1, SegFormer-B0, SegFormer-B5, ViT-Adapter and PSPNet. Five classes were pre-defined for the segmentation, which could effectively guide the cutting system to locate the target, including the head, body, fins, tail of the catfish, and the image background. Overall, BEiTV1 demonstrated the poorest performance with 77.3% of mIoU (mean intersection-over-union) and 86.7% of MPA (mean pixel accuracy) among all tested models using the test data set, while SegFormer-B5 outperformed all others with 89.2% of mIoU and 94.6% of MPA on the catfish images. The inference speed for SegFormer-B5 was 0.278 sec per image at the resolution of 640x640. The proposed deep learning-based sensing system is expected to be a reliable tool for automating the catfish cutting process.

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Section: Deep Learning Models For Semantic Segmentationmentioning

confidence: 99%

Automating catfish cutting process using deep learning-based semantic segmentation

Thayananthan

Zhang

Liu

et al. 2023

Sensing for Agriculture and Food Quality and Safety XV

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“…To maximize the commercial values, the deheading machines should leave the maximum amount of meat on the fillet and ensure no part of the fish gills, and the head skeleton is included in the fillet [ 39 , 40 , 41 , 42 ]. The main deheading position can be classified into four classes, as shown in Figure 1 , namely straight cutting, slant cutting, V-cutting, and round cutting along the gill area [ 43 , 44 ]. Commercial machines, like Baader 166, have been used in industry and research [ 39 ].…”

Section: Modern Fish Cuttingmentioning

confidence: 99%

Cutting Techniques in the Fish Industry: A Critical Review

Liu

Lyu

et al. 2022

Foods

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Fish and fishery products are among the most important sources of nutritional components for human health, including high-quality proteins, essential vitamins, minerals, and healthy polyunsaturated fatty acids. Fish farming and processing technologies are continuously evolving to improve and enhance the appearance, yield, and quality of fish and fish products from farm to fork throughout the fish supply chain, including growth, postharvest, treatment, storage, transportation, and distribution. Processing of fish involves a period of food withdrawal, collection and transportation, the process of stunning, bleeding, chilling, cutting, packaging, and byproduct recycling. Cutting is a set of crucial operations in fish processing to divide the whole fish into smaller pieces for producing fish products (e.g., fish fillets, steaks, etc.). Various techniques and machinery have been introduced in the field to advance and automate cutting operations. This review aims to provide a comprehensive review of fish cutting techniques, machine vision and artificial intelligence applications, and future directions in fish industries. This paper is expected to stimulate research on enhancing fish cutting yield, product diversity, safety and quality, as well as providing advanced solutions for engineering problems encountered in the fish industry.

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Intelligent Cutting in Fish Processing: Efficient, High-quality, and Safe Production of Fish Products

Fang

2023

Food Bioprocess Technol

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The effect of cutting and fish‐orientation systems on the deheading yield of carp

Cited by 4 publications

References 1 publication

Automating catfish cutting process using deep learning-based semantic segmentation

Automating catfish cutting process using deep learning-based semantic segmentation

Cutting Techniques in the Fish Industry: A Critical Review

Intelligent Cutting in Fish Processing: Efficient, High-quality, and Safe Production of Fish Products

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