Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach
Willd.) is due to its high protein content and gluten-free condition; nonetheless, the presence of foreign bodies in quinoa processing facilities is an issue that must be addressed. As a result, convolutional neural networks have been adopted, mostly because of their data extraction capabilities, wh...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2023
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| author | Avila-George, Himer De‐la‐Torre, Miguel Sanchez Garces, Jorge Alejandro Coaquira Quispe, Joel Jerson Prieto, Jose Manuel Castro, Wilson |
| author_browse | Avila-George, Himer Castro, Wilson Coaquira Quispe, Joel Jerson De‐la‐Torre, Miguel Prieto, Jose Manuel Sanchez Garces, Jorge Alejandro |
| author_facet | Avila-George, Himer De‐la‐Torre, Miguel Sanchez Garces, Jorge Alejandro Coaquira Quispe, Joel Jerson Prieto, Jose Manuel Castro, Wilson |
| author_sort | Avila-George, Himer |
| collection | Producción científica |
| description | Willd.) is due to its high protein content and gluten-free condition; nonetheless, the presence of foreign bodies in quinoa processing facilities is an issue that must be addressed. As a result, convolutional neural networks have been adopted, mostly because of their data extraction capabilities, which had not been utilized before for this purpose. Consequently, the main objective of this work is to evaluate convolutional neural networks with a learning transfer for foreign bodies identification in quinoa samples. For experimentation, quinoa samples were collected and manually split into 17 classes: quinoa grains and 16 foreign bodies. Then, one thousand images were obtained from each class in RGB space and transformed into four different color spaces (L*a*b*, HSV, YCbCr, and Gray). Three convolutional neural networks (AlexNet, MobileNetv2, and DenseNet-201) were trained using the five color spaces, and the evaluation results were expressed in terms of accuracy and F-score. All the CNN approaches compared showed an F-score ranging from 98% to 99%; both color space and CNN structure were found to have significant effects on the F-score. Also, DenseNet-201 was the most robust architecture and, at the same time, the most time-consuming. These results evidence the capacity of CNN architectures to be used for the discrimination of foreign bodies in quinoa processing facilities. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/652 |
| institution | UPeU |
| language | English |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/6522026-10-30T16:56:46ZDiscrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approachAvila-George, HimerDe‐la‐Torre, MiguelSanchez Garces, Jorge AlejandroCoaquira Quispe, Joel JersonPrieto, Jose ManuelCastro, WilsonImage processingDiscriminationPost-harvestQuinoaTransfer LearningDeep LearningConvolutional Neural NetworksAlexnetMobilenetv2Densenet-201Willd.) is due to its high protein content and gluten-free condition; nonetheless, the presence of foreign bodies in quinoa processing facilities is an issue that must be addressed. As a result, convolutional neural networks have been adopted, mostly because of their data extraction capabilities, which had not been utilized before for this purpose. Consequently, the main objective of this work is to evaluate convolutional neural networks with a learning transfer for foreign bodies identification in quinoa samples. For experimentation, quinoa samples were collected and manually split into 17 classes: quinoa grains and 16 foreign bodies. Then, one thousand images were obtained from each class in RGB space and transformed into four different color spaces (L*a*b*, HSV, YCbCr, and Gray). Three convolutional neural networks (AlexNet, MobileNetv2, and DenseNet-201) were trained using the five color spaces, and the evaluation results were expressed in terms of accuracy and F-score. All the CNN approaches compared showed an F-score ranging from 98% to 99%; both color space and CNN structure were found to have significant effects on the F-score. Also, DenseNet-201 was the most robust architecture and, at the same time, the most time-consuming. These results evidence the capacity of CNN architectures to be used for the discrimination of foreign bodies in quinoa processing facilities.Articlehttps://pubmed.ncbi.nlm.nih.gov/36743959https://doi.org/10.7717/peerj.14808PMC989390710.7717/peerj.14808https://hdl.handle.net/20.500.15037/65236743959W4318473565enPeerJ2167-83592023-01-302026-06-13T09:29:06Z2026-06-13T09:29:06Z |
| spellingShingle | Image processing Discrimination Post-harvest Quinoa Transfer Learning Deep Learning Convolutional Neural Networks Alexnet Mobilenetv2 Densenet-201 Avila-George, Himer De‐la‐Torre, Miguel Sanchez Garces, Jorge Alejandro Coaquira Quispe, Joel Jerson Prieto, Jose Manuel Castro, Wilson Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach |
| title | Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach |
| title_full | Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach |
| title_fullStr | Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach |
| title_full_unstemmed | Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach |
| title_short | Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach |
| title_sort | discrimination of foreign bodies in quinoa chenopodium quinoa willd grains using convolutional neural networks with a transfer learning approach |
| topic | Image processing Discrimination Post-harvest Quinoa Transfer Learning Deep Learning Convolutional Neural Networks Alexnet Mobilenetv2 Densenet-201 |
| topic_facet | Image processing Discrimination Post-harvest Quinoa Transfer Learning Deep Learning Convolutional Neural Networks Alexnet Mobilenetv2 Densenet-201 |
| url | https://pubmed.ncbi.nlm.nih.gov/36743959 https://doi.org/10.7717/peerj.14808 https://hdl.handle.net/20.500.15037/652 |
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