Classification of fish freshness using a convolutional neural network
Abstract At work, the freshness of rainbow trout (Oncorhynchus mykiss) was analyzed, this aspect being very important to determine its quality. The objective is to propose a computational model based on a CNN to classify the freshness of the trout based on changes in the color of their eyes and gill...
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| Autores principales: | , |
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| Formato: | Preprint |
| Lenguaje: | inglés |
| Publicado: |
2023
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| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| _version_ | 1881700465655152640 |
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| author | Edwin Wilson Mamani Flores Roger Pedraza Huisa |
| author_browse | Edwin Wilson Mamani Flores Roger Pedraza Huisa |
| author_facet | Edwin Wilson Mamani Flores Roger Pedraza Huisa |
| author_sort | Edwin Wilson Mamani Flores |
| collection | Producción científica |
| description | Abstract At work, the freshness of rainbow trout (Oncorhynchus mykiss) was analyzed, this aspect being very important to determine its quality. The objective is to propose a computational model based on a CNN to classify the freshness of the trout based on changes in the color of their eyes and gills. For this purpose, a dataset of images with acquired trout was created. To obtain the results, three experiments were conducted: the first with 2 classes (days 1 and 9), the second with 3 classes (days 1, 5, and 9), and the third with 5 classes (days 1, 3, 5, 7, and 9). All experi- ments were carried out in Google Colab. The results were validated using a confusion matrix ROC curve. The best results were obtained by the ResNeXt5032x4d model, with 2 classes achieving an accuracy of 0.9833, with 3 classes an accuracy of 0.9222, and with 5 classes an accuracy of 0.8800. |
| format | Preprint |
| id | openalex:W4389614803 |
| institution | UPeU |
| language | English |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | openalex:W4389614803Classification of fish freshness using a convolutional neural networkEdwin Wilson Mamani FloresRoger Pedraza Huisafish freshness classificationrainbow trouteye colorfish qualityconvolutional neural networksAdvanced Neural Network ApplicationsWater Quality Monitoring TechnologiesSmart Agriculture and AIAbstract At work, the freshness of rainbow trout (Oncorhynchus mykiss) was analyzed, this aspect being very important to determine its quality. The objective is to propose a computational model based on a CNN to classify the freshness of the trout based on changes in the color of their eyes and gills. For this purpose, a dataset of images with acquired trout was created. To obtain the results, three experiments were conducted: the first with 2 classes (days 1 and 9), the second with 3 classes (days 1, 5, and 9), and the third with 5 classes (days 1, 3, 5, 7, and 9). All experi- ments were carried out in Google Colab. The results were validated using a confusion matrix ROC curve. The best results were obtained by the ResNeXt5032x4d model, with 2 classes achieving an accuracy of 0.9833, with 3 classes an accuracy of 0.9222, and with 5 classes an accuracy of 0.8800.Preprinthttps://doi.org/10.21203/rs.3.rs-3547262/v4W4389614803enResearch Square2023-12-12 |
| spellingShingle | fish freshness classification rainbow trout eye color fish quality convolutional neural networks Advanced Neural Network Applications Water Quality Monitoring Technologies Smart Agriculture and AI Edwin Wilson Mamani Flores Roger Pedraza Huisa Classification of fish freshness using a convolutional neural network |
| title | Classification of fish freshness using a convolutional neural network |
| title_full | Classification of fish freshness using a convolutional neural network |
| title_fullStr | Classification of fish freshness using a convolutional neural network |
| title_full_unstemmed | Classification of fish freshness using a convolutional neural network |
| title_short | Classification of fish freshness using a convolutional neural network |
| title_sort | classification of fish freshness using a convolutional neural network |
| topic | fish freshness classification rainbow trout eye color fish quality convolutional neural networks Advanced Neural Network Applications Water Quality Monitoring Technologies Smart Agriculture and AI |
| topic_facet | fish freshness classification rainbow trout eye color fish quality convolutional neural networks Advanced Neural Network Applications Water Quality Monitoring Technologies Smart Agriculture and AI |
| url | https://doi.org/10.21203/rs.3.rs-3547262/v4 |
| work_keys_str_mv | AT edwinwilsonmamaniflores classificationoffishfreshnessusingaconvolutionalneuralnetwork AT rogerpedrazahuisa classificationoffishfreshnessusingaconvolutionalneuralnetwork |