Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/12/21/2938 |
| _version_ | 1797469388697239552 |
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| author | Daoliang Li Xin Li Qi Wang Yinfeng Hao |
| author_facet | Daoliang Li Xin Li Qi Wang Yinfeng Hao |
| author_sort | Daoliang Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:20:42Z |
| format | Article |
| id | doaj.art-195530edaefe48f4a3ea357d531d2c9d |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-09T19:20:42Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-195530edaefe48f4a3ea357d531d2c9d2023-11-24T03:24:11ZengMDPI AGAnimals2076-26152022-10-011221293810.3390/ani12212938Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A ReviewDaoliang Li0Xin Li1Qi Wang2Yinfeng Hao3National Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, Beijing 100083, ChinaNational Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, Beijing 100083, ChinaNational Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, Beijing 100083, ChinaNational Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, Beijing 100083, ChinaAquatic 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.https://www.mdpi.com/2076-2615/12/21/2938computer visionfish diseaseimage processingintelligent diagnosisreal-time detection |
| spellingShingle | Daoliang Li Xin Li Qi Wang Yinfeng Hao Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review Animals computer vision fish disease image processing intelligent diagnosis real-time detection |
| title | Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review |
| title_full | Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review |
| title_fullStr | Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review |
| title_full_unstemmed | Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review |
| title_short | Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review |
| title_sort | advanced techniques for the intelligent diagnosis of fish diseases a review |
| topic | computer vision fish disease image processing intelligent diagnosis real-time detection |
| url | https://www.mdpi.com/2076-2615/12/21/2938 |
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