Classification of Salmon Freshness In Situ Using Convolutional Neural Network
Fish is an important food resource, an economic contributor, and a staple food for Filipinos. For the safety and satisfaction of consumers, fish freshness must be determined. Using the convolutional neural network (CNN) algorithm, we determined salmon fillet freshness in this study. In total, 7000 i...
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MDPI AG
2025-04-01
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| Series: | Engineering Proceedings |
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| author | Juan Miguel L. Valeriano Carlos C. Hortinela |
| author_facet | Juan Miguel L. Valeriano Carlos C. Hortinela |
| author_sort | Juan Miguel L. Valeriano |
| collection | DOAJ |
| description | Fish is an important food resource, an economic contributor, and a staple food for Filipinos. For the safety and satisfaction of consumers, fish freshness must be determined. Using the convolutional neural network (CNN) algorithm, we determined salmon fillet freshness in this study. In total, 7000 images were used for training and 40 for testing the CNN model. The deep learning technique, specifically ResNet50 architecture, was used with Raspberry Pi 4B, and Raspberry Pi camera V<sub>2</sub> was employed to take images of fish. The model showed a 92.5% accuracy, highlighting the CNN model’s accurate evaluation of seafood quality. |
| format | Article |
| id | doaj-art-1f68399925424b51a1e89837735dd236 |
| institution | Matheson Library |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-1f68399925424b51a1e89837735dd2362025-06-25T13:48:02ZengMDPI AGEngineering Proceedings2673-45912025-04-019211210.3390/engproc2025092012Classification of Salmon Freshness In Situ Using Convolutional Neural NetworkJuan Miguel L. Valeriano0Carlos C. Hortinela1School of Electrical, Electronics, and Computer Engineering, Mapua University, Manila 1002, PhilippinesSchool of Electrical, Electronics, and Computer Engineering, Mapua University, Manila 1002, PhilippinesFish is an important food resource, an economic contributor, and a staple food for Filipinos. For the safety and satisfaction of consumers, fish freshness must be determined. Using the convolutional neural network (CNN) algorithm, we determined salmon fillet freshness in this study. In total, 7000 images were used for training and 40 for testing the CNN model. The deep learning technique, specifically ResNet50 architecture, was used with Raspberry Pi 4B, and Raspberry Pi camera V<sub>2</sub> was employed to take images of fish. The model showed a 92.5% accuracy, highlighting the CNN model’s accurate evaluation of seafood quality.https://www.mdpi.com/2673-4591/92/1/12convolutional neural networkimage processingResNet50salmonfish freshness |
| spellingShingle | Juan Miguel L. Valeriano Carlos C. Hortinela Classification of Salmon Freshness In Situ Using Convolutional Neural Network Engineering Proceedings convolutional neural network image processing ResNet50 salmon fish freshness |
| title | Classification of Salmon Freshness In Situ Using Convolutional Neural Network |
| title_full | Classification of Salmon Freshness In Situ Using Convolutional Neural Network |
| title_fullStr | Classification of Salmon Freshness In Situ Using Convolutional Neural Network |
| title_full_unstemmed | Classification of Salmon Freshness In Situ Using Convolutional Neural Network |
| title_short | Classification of Salmon Freshness In Situ Using Convolutional Neural Network |
| title_sort | classification of salmon freshness in situ using convolutional neural network |
| topic | convolutional neural network image processing ResNet50 salmon fish freshness |
| url | https://www.mdpi.com/2673-4591/92/1/12 |
| work_keys_str_mv | AT juanmiguellvaleriano classificationofsalmonfreshnessinsituusingconvolutionalneuralnetwork AT carloschortinela classificationofsalmonfreshnessinsituusingconvolutionalneuralnetwork |