Design and implementation of remote fish disease video diagnosis expert system

Ma, Yinchi; Ding, Wen

doi:10.1109/iccsn.2017.8230192

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…The real situation of the fish disease site and the degree of water pollution cannot be transmitted to experts through mobile phones, and the use of aquaculture surveillance video could overcome these problems to a certain extent. Ma et al proposed a remote fish disease video-based diagnosis expert system [ 78 ], which used remote video to provide experts with information on the aquaculture environment and dynamic fish conditions, and realized accurate and timely diagnosis of fish diseases by remote experts through direct communication between field personnel and experts.…”

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%