Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis
The expansion of aquaculture necessitates innovative disease detection methods to ensure sustainable production. Fish diseases caused by bacteria, viruses, fungi, and parasites result in significant economic losses and threaten food security. Traditional detection methods are labor-intensive and tim...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Aquaculture Journal |
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| Online Access: | https://www.mdpi.com/2673-9496/5/1/6 |
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| author | Hayin Tamut Robin Ghosh Kamal Gosh Md Abdus Salam Siddique |
| author_facet | Hayin Tamut Robin Ghosh Kamal Gosh Md Abdus Salam Siddique |
| author_sort | Hayin Tamut |
| collection | DOAJ |
| description | The expansion of aquaculture necessitates innovative disease detection methods to ensure sustainable production. Fish diseases caused by bacteria, viruses, fungi, and parasites result in significant economic losses and threaten food security. Traditional detection methods are labor-intensive and time-consuming, emphasizing the need for automated approaches. This study investigates the application of convolutional neural networks (CNNs) for classifying freshwater fish diseases. Such CNNs offer an efficient and automated solution for fish disease detection, reducing the burden on aquatic health experts and enabling timely interventions to mitigate economic losses. A dataset of 2444 images was used across seven classes—bacterial red disease, bacterial Aeromoniasis disease, bacterial gill disease, fungal disease, parasitic diseases, white tail disease, and healthy fish. The CNNs model incorporates convolutional layers for feature extraction, max-pooling for down-sampling, dense layers for classification, and dropout for regularization. Categorical cross-entropy loss and the Adam optimizer were used over 50 epochs, with continuous training and validation performance monitoring. The results indicated that the model achieved an accuracy of 99.71% and a test loss of 0.0119. This study highlights the transformative potential of artificial intelligence in aquaculture for enhancing food security. |
| format | Article |
| id | doaj-art-b67d0eb8511a4d799f50f58b0a64c9c9 |
| institution | Kabale University |
| issn | 2673-9496 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aquaculture Journal |
| spelling | doaj-art-b67d0eb8511a4d799f50f58b0a64c9c92025-08-20T03:43:14ZengMDPI AGAquaculture Journal2673-94962025-03-0151610.3390/aquacj5010006Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks AnalysisHayin Tamut0Robin Ghosh1Kamal Gosh2Md Abdus Salam Siddique3Department of Engineering and Computing Sciences, Arkansas Tech University, Russellville, AR 72801, USADepartment of Engineering and Computing Sciences, Arkansas Tech University, Russellville, AR 72801, USADepartment of Agriculture and Natural Resources, Langston University, Langston, OK 73050, USADepartment of Engineering and Computing Sciences, Arkansas Tech University, Russellville, AR 72801, USAThe expansion of aquaculture necessitates innovative disease detection methods to ensure sustainable production. Fish diseases caused by bacteria, viruses, fungi, and parasites result in significant economic losses and threaten food security. Traditional detection methods are labor-intensive and time-consuming, emphasizing the need for automated approaches. This study investigates the application of convolutional neural networks (CNNs) for classifying freshwater fish diseases. Such CNNs offer an efficient and automated solution for fish disease detection, reducing the burden on aquatic health experts and enabling timely interventions to mitigate economic losses. A dataset of 2444 images was used across seven classes—bacterial red disease, bacterial Aeromoniasis disease, bacterial gill disease, fungal disease, parasitic diseases, white tail disease, and healthy fish. The CNNs model incorporates convolutional layers for feature extraction, max-pooling for down-sampling, dense layers for classification, and dropout for regularization. Categorical cross-entropy loss and the Adam optimizer were used over 50 epochs, with continuous training and validation performance monitoring. The results indicated that the model achieved an accuracy of 99.71% and a test loss of 0.0119. This study highlights the transformative potential of artificial intelligence in aquaculture for enhancing food security.https://www.mdpi.com/2673-9496/5/1/6aquacultureconvolutional neural networks (CNN)disease detectionfreshwater fishimage classificationdeep learning |
| spellingShingle | Hayin Tamut Robin Ghosh Kamal Gosh Md Abdus Salam Siddique Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis Aquaculture Journal aquaculture convolutional neural networks (CNN) disease detection freshwater fish image classification deep learning |
| title | Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis |
| title_full | Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis |
| title_fullStr | Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis |
| title_full_unstemmed | Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis |
| title_short | Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis |
| title_sort | enhancing disease detection in the aquaculture sector using convolutional neural networks analysis |
| topic | aquaculture convolutional neural networks (CNN) disease detection freshwater fish image classification deep learning |
| url | https://www.mdpi.com/2673-9496/5/1/6 |
| work_keys_str_mv | AT hayintamut enhancingdiseasedetectionintheaquaculturesectorusingconvolutionalneuralnetworksanalysis AT robinghosh enhancingdiseasedetectionintheaquaculturesectorusingconvolutionalneuralnetworksanalysis AT kamalgosh enhancingdiseasedetectionintheaquaculturesectorusingconvolutionalneuralnetworksanalysis AT mdabdussalamsiddique enhancingdiseasedetectionintheaquaculturesectorusingconvolutionalneuralnetworksanalysis |