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NAAS Journal 2026

International Journal Of Forest, Animal And Fisheries Research(IJFAF)

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  3. Vol-9,Issue-2, April - June 2025
  4. Deep Learning and AI Tools for Monitoring and Detecting Diseases in Freshwater Fish Populations

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Deep Learning and AI Tools for Monitoring and Detecting Diseases in Freshwater Fish Populations

Koteshwar Rao

Article Info: Received: 14 Apr 2025; Received in revised form: 10 May 2025; Accepted: 17 May 2025; Available online: 21 May 2025

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DOI: 10.22161/ijfaf.9.2.3

Journal : International Journal Of Forest, Animal And Fisheries Research(IJFAF)

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Freshwater fish populations sustainability and well-being are essential to aquaculture biodiversity and food security conventional approaches to fish disease diagnosis are frequently labor-intensive time-consuming and necessitate professional intervention which causes treatment delays and large financial losses recent developments in deep learning dl a subfield of artificial intelligence AI present viable substitutes for automated quick and precise fish disease detection this study investigates how to use AI and deep learning tools to monitor and diagnose illnesses that impact freshwater fish predictive modeling pattern recognition and image recognition techniques are used by these systems to accurately identify visual symptoms like lesions discoloration or abnormal behavior along with their datasets training procedures and performance metrics the paper examines a variety of machine learning models used in fish health assessment such as convolutional neural networks CNNS support vector machines SVMS and hybrid architectures real-time monitoring systems made possible by internet of things IOT gadgets and AI-powered image processing frameworks are also covered the results show how deep learning can transform aquaculture disease management by improving fish welfare enabling early detection and lowering manual labor the development of robust scalable and economical solutions is one of the future directions

Artificial Intelligence (AI), Deep Learning, Machine Learning, Fish Disease, Freshwater Fish, Aquaculture, Convolutional Neural Networks (CNNs).

[1] Ahmed, M. A., et al. Deep Learning-Based Fish Disease Identification Using Convolutional Neural Network. Journal of Ambient Intelligence and Humanized Computing, 12, 10425–10435. 2021. https://doi.org/10.1007/s12652-020-02651-1.
[2] Rathi, D., & Jain, D. Fish Disease Detection Using Deep Learning Techniques: A Review. Procedia Computer Science, 179, 958–965.2021. https://doi.org/10.1016/j.procs.2021.01.081.
[3] Uddin, M. R., et al. Smart Fish Disease Detection System Using Image Processing and IoT-Based Environmental Monitoring. Sustainable Computing: Informatics and Systems, 35, 100735. 2022. https://doi.org/10.1016/j.suscom.2022.100735.
[4] He, K., et al. Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. 2016. https://doi.org/10.1109/CVPR.2016.90.
[5] Wang, H., et al. A Real-Time Monitoring System for Water Quality Based on Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 15(4), 1–10. 2019. https://doi.org/10.1177/1550147719841431.
[6] Al-Garadi, M. A., et al. Internet of Things (IoT) and Artificial Intelligence (AI) Applications for Smart Aquaculture. Computers and Electronics in Agriculture, 189, 106414. 2021. https://doi.org/10.1016/j.compag.2021.106414.
[7] Saleh, B., et al. A Convolutional Neural Network Approach for Automatic Detection of Fish Diseases. IEEE Access, 8, 106575–106585. 2020. https://doi.org/10.1109/ACCESS.2020.2998514.
[8] Zhang, Y., et al. Environmental Parameter-Based Fish Disease Detection Using LSTM Networks. Sensors, 21(6), 2005.2021.https://doi.org/10.3390/s21062005.
[9] Shuvo, M. H., et al. Fish Disease Detection Using IoT and Machine Learning. IEEE Internet of Things Journal, 9(14), 12347–12358. 2022. https://doi.org/10.1109/JIOT.2021.3126741.
[10] Li, Z., et al. Multimodal Data Fusion for Aquaculture Monitoring Using Deep Learning. Information Fusion, 67, 104–116. 2021. https://doi.org/10.1016/j.inffus.2020.10.003.
[11] Dutta, A., et al. Vision-Based Fish Disease Recognition System Using Deep Neural Networks. Journal of King Saud University - Computer and Information Sciences, 34(5), 1967–1974.2022. https://doi.org/10.1016/j.jksuci.2020.09.004.
[12] Abdelrahman, H. A., El Halaby, E., & Farag, A. A. Deep Learning Approaches for Fish Disease Diagnosis: A Survey. Aquaculture Reports, 18, 100500. 2020. https://doi.org/10.1016/j.aqrep.2020.100500.
[13] ISO/IEC JTC 1/SC 42. Artificial Intelligence — Use Cases in Aquaculture. 2021. International Organization for Standardization Technical Report.

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