Classification of fish freshness using a convolutional neural network
Abstract In the work, the freshness of the 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 the changes in 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_ | 1881700464876060672 |
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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 In the work, the freshness of the 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 the changes in color of their eyes and gills. For this, an image data set was created with the acquired trout. To obtain the results, 3 experiments were carried out, 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 the experiments were run in Google colab.The results were validated with a confusion matrix and ROC curve. The best results were given by the ResNeXt5032x4d model, with 2 classes it obtained an accuracy of 0.9833, 3 classes an accuracy of 0.9222 and with 5 classes an accuracy of 0.8800. |
| format | Preprint |
| id | openalex:W4388465061 |
| institution | UPeU |
| language | English |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | openalex:W4388465061Classification of fish freshness using a convolutional neural networkEdwin Wilson Mamani FloresRoger Pedraza Huisafish freshness classificationtroutrainbow trouteye colorfish qualityconvolutional neural networksAdvanced Neural Network ApplicationsWater Quality Monitoring TechnologiesMeat and Animal Product QualityAbstract In the work, the freshness of the 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 the changes in color of their eyes and gills. For this, an image data set was created with the acquired trout. To obtain the results, 3 experiments were carried out, 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 the experiments were run in Google colab.The results were validated with a confusion matrix and ROC curve. The best results were given by the ResNeXt5032x4d model, with 2 classes it obtained an accuracy of 0.9833, 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/v1W4388465061enResearch Square2023-11-07 |
| spellingShingle | fish freshness classification trout rainbow trout eye color fish quality convolutional neural networks Advanced Neural Network Applications Water Quality Monitoring Technologies Meat and Animal Product Quality 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 trout rainbow trout eye color fish quality convolutional neural networks Advanced Neural Network Applications Water Quality Monitoring Technologies Meat and Animal Product Quality |
| topic_facet | fish freshness classification trout rainbow trout eye color fish quality convolutional neural networks Advanced Neural Network Applications Water Quality Monitoring Technologies Meat and Animal Product Quality |
| url | https://doi.org/10.21203/rs.3.rs-3547262/v1 |
| work_keys_str_mv | AT edwinwilsonmamaniflores classificationoffishfreshnessusingaconvolutionalneuralnetwork AT rogerpedrazahuisa classificationoffishfreshnessusingaconvolutionalneuralnetwork |