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Smart Aquaculture: A Real-Time Fish Disease Detection System
Authors
1 3 4 5Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India. 2 Assistant Professor, Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 159-164
- Article Type
- Research Article
- Check for updates
-
References
1. S. Malik, T. Kumar and A. K. Sahoo, “Image Processing Techniques for Identification of Fish Disease,” in Proc. 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), Singapore, 2017, pp. 55– 59, doi:10.1109/SIPROCESS.2017.8124505.
2. S. Biswas, D. Muduli, M. A. Islam, A. S. Kanade, A. T. Zamani, S. P. Kanade, and N.Parveen, “Empirical Evaluation of DeepLearning Techniques for Fish Disease Detection in Aquaculture Systems: A Transfer Learning and Fusion-Based Approach,” IEEE Access, vol. 12, pp. 1–?, 2024, doi:10.1109/ACCESS.2024.3504283.
3. J.-H. Wang, S.-K. Lee, Y.-C. Lai, C.-C. Lin, T.- Y. Wang, Y.-R. Lin, T.-H. Hsu, C.-W. Huang, and C.-P. Chiang, “Anomalous Behavioral Detection in Underwater Fish,” IEEE Access, vol. 8, pp. 3043712-?, 2020, doi:10.1109/ACCESS.2020.3043712.
4. Waleed, H. Medhat, M. Esmail, K. Osama,Samy and T. M. Ghanim, “Automatic Recognition of Fish Diseases in Fish Farms,” in Proc. 2019 14th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, Dec. 2019, pp. 201–206, doi: 10.1109/ICCES48960.2019.9068141.
5. S. Dash, S. Ojha, R. K. Muduli, S. P. Patra and Barik, “Fish Type and Disease Classification Using Deep Learning Model Based Customized CNN with ResNet-50 Mechanism for Fish Disease Recognition in Aquaculture,” IEEE Access, vol. 11, pp. 1–?, 2023, doi:10.1109/ACCESS.2023.3280540.
6. M. Liu, Z. Zhao, R. Li and C. Lin, "YOLO-Fish Class: A Taxonomic Model for Underwater Economic Fish with Excellent Performance," 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Compiegne, France, 2025, pp. 1494-1500, doi: 10.1109/CSCWD64889.2025.11033592.
7. Y.-P. Huang, S. P. Khabusi, et al., “A CNN-OSELM Multi-Layer Fusion Network With Attention Mechanism for Fish Disease Recognition in Aquaculture,” IEEE Access, vol.11, pp. xxxx–xxxx, 2023, doi:10.1109/ACCESS.2023.3280540.
8. J. Sikder, K. I. Sarek and U. K. Das, “Fish Disease Detection System: A Case Study of Freshwater Fishes of Bangladesh,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, pp. 867–871, Jun. 2021, doi:10.14569/IJACSA.2021.01206100.
9. M. S. Ahmed, T. T. Aurpa, and M. A. K. Azad, “Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture,” arXiv preprint arXiv:2105.03934, May 2021
10. A.Chatziantoniou, N. Papandroulakis, O. Stavrakidis-Zachou, S. Spondylidis, S. Taskaris, and K. Topouzelis, “Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming,” Applied Sciences, vol. 13, no. 6122, May 2023, doi:10.3390/app13106122.
11. M. K. S. Reddy and B. S. Rao, “Detection of Fish Disease Using Machine Learning Techniques,” International Journal of Engineering Science and Advanced Technology (IJESAT), vol. 21, no. 3, pp. 117– 123, 2023.
12. M. R. I. Mamun, U. S. Rahman, T. Akter and M. A. Azim, “Fish Disease Detection using Deep Learning and Machine Learning,” International Journal of Computer Applications, vol. 185, no. 36, pp. 1–9, Oct 2023, doi:10.5120/32920-923079
13. R. M S, M. A. H, J. K, S. M. Sohail and M. N, "Review on Fresh Water Fish Disease Detection using Image Processing," 2025 International Conference on Future Technologies (ICFT), Sangli, India, 2025, pp. 1-6, doi: 10.1109/ICFT66708.2025.11336456
14. S.Bhattacharjee, H. Murad, and S. C. Tista, “Towards Developing a Fish Disease Recognizer: A Deep Learning-Based Approach,” in Proc. 2024 6th Int. Conf. Sustainable Technol. for Industry 5.0 (STI), Narayanganj, Bangladesh, Dec. 2024, pp. 1–6
15. Lazhar Khriji, Abdelmalek Kouadri, and Majdi Mansouri, “Deep Learning based Fish Health Monitoring and Diagnosis: A Review,” IEEE Access, vol. 13, pp. 181 599–181 616, Oct. 2025, doi: 10.1109/ACCESS.2025.3621487.
2. S. Biswas, D. Muduli, M. A. Islam, A. S. Kanade, A. T. Zamani, S. P. Kanade, and N.Parveen, “Empirical Evaluation of DeepLearning Techniques for Fish Disease Detection in Aquaculture Systems: A Transfer Learning and Fusion-Based Approach,” IEEE Access, vol. 12, pp. 1–?, 2024, doi:10.1109/ACCESS.2024.3504283.
3. J.-H. Wang, S.-K. Lee, Y.-C. Lai, C.-C. Lin, T.- Y. Wang, Y.-R. Lin, T.-H. Hsu, C.-W. Huang, and C.-P. Chiang, “Anomalous Behavioral Detection in Underwater Fish,” IEEE Access, vol. 8, pp. 3043712-?, 2020, doi:10.1109/ACCESS.2020.3043712.
4. Waleed, H. Medhat, M. Esmail, K. Osama,Samy and T. M. Ghanim, “Automatic Recognition of Fish Diseases in Fish Farms,” in Proc. 2019 14th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, Dec. 2019, pp. 201–206, doi: 10.1109/ICCES48960.2019.9068141.
5. S. Dash, S. Ojha, R. K. Muduli, S. P. Patra and Barik, “Fish Type and Disease Classification Using Deep Learning Model Based Customized CNN with ResNet-50 Mechanism for Fish Disease Recognition in Aquaculture,” IEEE Access, vol. 11, pp. 1–?, 2023, doi:10.1109/ACCESS.2023.3280540.
6. M. Liu, Z. Zhao, R. Li and C. Lin, "YOLO-Fish Class: A Taxonomic Model for Underwater Economic Fish with Excellent Performance," 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Compiegne, France, 2025, pp. 1494-1500, doi: 10.1109/CSCWD64889.2025.11033592.
7. Y.-P. Huang, S. P. Khabusi, et al., “A CNN-OSELM Multi-Layer Fusion Network With Attention Mechanism for Fish Disease Recognition in Aquaculture,” IEEE Access, vol.11, pp. xxxx–xxxx, 2023, doi:10.1109/ACCESS.2023.3280540.
8. J. Sikder, K. I. Sarek and U. K. Das, “Fish Disease Detection System: A Case Study of Freshwater Fishes of Bangladesh,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, pp. 867–871, Jun. 2021, doi:10.14569/IJACSA.2021.01206100.
9. M. S. Ahmed, T. T. Aurpa, and M. A. K. Azad, “Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture,” arXiv preprint arXiv:2105.03934, May 2021
10. A.Chatziantoniou, N. Papandroulakis, O. Stavrakidis-Zachou, S. Spondylidis, S. Taskaris, and K. Topouzelis, “Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming,” Applied Sciences, vol. 13, no. 6122, May 2023, doi:10.3390/app13106122.
11. M. K. S. Reddy and B. S. Rao, “Detection of Fish Disease Using Machine Learning Techniques,” International Journal of Engineering Science and Advanced Technology (IJESAT), vol. 21, no. 3, pp. 117– 123, 2023.
12. M. R. I. Mamun, U. S. Rahman, T. Akter and M. A. Azim, “Fish Disease Detection using Deep Learning and Machine Learning,” International Journal of Computer Applications, vol. 185, no. 36, pp. 1–9, Oct 2023, doi:10.5120/32920-923079
13. R. M S, M. A. H, J. K, S. M. Sohail and M. N, "Review on Fresh Water Fish Disease Detection using Image Processing," 2025 International Conference on Future Technologies (ICFT), Sangli, India, 2025, pp. 1-6, doi: 10.1109/ICFT66708.2025.11336456
14. S.Bhattacharjee, H. Murad, and S. C. Tista, “Towards Developing a Fish Disease Recognizer: A Deep Learning-Based Approach,” in Proc. 2024 6th Int. Conf. Sustainable Technol. for Industry 5.0 (STI), Narayanganj, Bangladesh, Dec. 2024, pp. 1–6
15. Lazhar Khriji, Abdelmalek Kouadri, and Majdi Mansouri, “Deep Learning based Fish Health Monitoring and Diagnosis: A Review,” IEEE Access, vol. 13, pp. 181 599–181 616, Oct. 2025, doi: 10.1109/ACCESS.2025.3621487.
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