Determination of Injury Rate on Fish Surface Based on Fuzzy C-means Clustering Algorithm and L*a*b* Color Space Using ZED Stereo Camera

Tran, Minh Thien; Kim, Dae Hwan; Kim, Chang Kyu; Kim, Hak Kyeong

doi:10.1109/urai.2018.8441790

Cited by 2 publications

(1 citation statement)

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“…The experiment was extended to assess epidermal contusions up and down skin tissue in six pond fish species to derive the frequency of injury occurrence. To detect the damage degree of the whole fish, Tran et al used the ZED stereo camera to determine the damage rate of the fish surface based on the fuzzy C-means clustering algorithm and L*a*b* color space [ 98 ]. The method took advantage of the L*a*b* color space and the fuzzy C-means clustering algorithm and used median, bilateral and Gaussian filters to improve the accuracy; the experimentally measured damage rates were close to the true damage rates.…”

Section: Intelligent Diagnosis Methods Of Fish Diseases Based On Imagesmentioning

confidence: 99%

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

Wang

et al. 2022

Animals

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Aquatic products, as essential sources of protein, have attracted considerable concern by producers and consumers. Precise fish disease prevention and treatment may provide not only healthy fish protein but also ecological and economic benefits. However, unlike intelligent two-dimensional diagnoses of plants and crops, one of the most serious challenges confronted in intelligent aquaculture diagnosis is its three-dimensional space. Expert systems have been applied to diagnose fish diseases in recent decades, allowing for restricted diagnosis of certain aquaculture. However, this method needs aquaculture professionals and specialists. In addition, diagnosis speed and efficiency are limited. Therefore, developing a new quick, automatic, and real-time diagnosis approach is very critical. The integration of image-processing and computer vision technology intelligently allows the diagnosis of fish diseases. This study comprehensively reviews image-processing technology and image-based fish disease detection methods, and analyzes the benefits and drawbacks of each diagnostic approach in different environments. Although it is widely acknowledged that there are many approaches for disease diagnosis and pathogen identification, some improvements in detection accuracy and speed are still needed. Constructing AR 3D images of fish diseases, standard and shared datasets, deep learning, and data fusion techniques will be helpful in improving the accuracy and speed of fish disease diagnosis.

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Section: Intelligent Diagnosis Methods Of Fish Diseases Based On Imagesmentioning

confidence: 99%