Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks
Early automatic detection of fungal infections in post-harvest citrus fruits is especially important for the citrus industry because only a few infected fruits can spread the infection to a whole batch during operations such as storage or exportation, thus causing great economic losses. Nowadays, th...
| Main Authors: | , , , , |
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| Format: | article |
| Language: | Inglés |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11939/5544 |
| _version_ | 1855032295744864256 |
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| author | Lorente, Delia Aleixos, Nuria Gómez-Sanchís, Juan Cubero, Sergio Blasco, José |
| author_browse | Aleixos, Nuria Blasco, José Cubero, Sergio Gómez-Sanchís, Juan Lorente, Delia |
| author_facet | Lorente, Delia Aleixos, Nuria Gómez-Sanchís, Juan Cubero, Sergio Blasco, José |
| author_sort | Lorente, Delia |
| collection | ReDivia |
| description | Early automatic detection of fungal infections in post-harvest citrus fruits is especially important for the citrus industry because only a few infected fruits can spread the infection to a whole batch during operations such as storage or exportation, thus causing great economic losses. Nowadays, this detection is carried out manually by trained workers illuminating the fruit with dangerous ultraviolet lighting. The use of hyperspectral imaging systems makes it possible to advance in the development of systems capable of carrying out this detection process automatically. However, these systems present the disadvantage of generating a huge amount of data, which must be selected in order to achieve a result that is useful to the sector. This work proposes a methodology to select features in multi-class classification problems using the receiver operating characteristic curve, in order to detect rottenness in citrus fruits by means of hyperspectral images. The classifier used is a multilayer perceptron, trained with a new learning algorithm called extreme learning machine. The results are obtained using images of mandarins with the pixels labelled in five different classes: two kinds of sound skin, two kinds of decay and scars. This method yields a reduced set of features and a classification success rate of around 89%. |
| format | article |
| id | ReDivia5544 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | ReDivia55442025-04-25T14:43:15Z Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks Lorente, Delia Aleixos, Nuria Gómez-Sanchís, Juan Cubero, Sergio Blasco, José Early automatic detection of fungal infections in post-harvest citrus fruits is especially important for the citrus industry because only a few infected fruits can spread the infection to a whole batch during operations such as storage or exportation, thus causing great economic losses. Nowadays, this detection is carried out manually by trained workers illuminating the fruit with dangerous ultraviolet lighting. The use of hyperspectral imaging systems makes it possible to advance in the development of systems capable of carrying out this detection process automatically. However, these systems present the disadvantage of generating a huge amount of data, which must be selected in order to achieve a result that is useful to the sector. This work proposes a methodology to select features in multi-class classification problems using the receiver operating characteristic curve, in order to detect rottenness in citrus fruits by means of hyperspectral images. The classifier used is a multilayer perceptron, trained with a new learning algorithm called extreme learning machine. The results are obtained using images of mandarins with the pixels labelled in five different classes: two kinds of sound skin, two kinds of decay and scars. This method yields a reduced set of features and a classification success rate of around 89%. 2017-06-01T10:12:32Z 2017-06-01T10:12:32Z 2013 FEB 2013 article acceptedVersion Lorente, Delia, Aleixos, N., Gomez-Sanchis, J., Cubero, Sergio, Blasco, J. (2013). Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks. Food and Bioprocess Technology, 6(2), 530-541. 1935-5130 http://hdl.handle.net/20.500.11939/5544 10.1007/s11947-011-0737-x en openAccess Impreso |
| spellingShingle | Lorente, Delia Aleixos, Nuria Gómez-Sanchís, Juan Cubero, Sergio Blasco, José Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks |
| title | Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks |
| title_full | Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks |
| title_fullStr | Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks |
| title_full_unstemmed | Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks |
| title_short | Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks |
| title_sort | selection of optimal wavelength features for decay detection in citrus fruit using the roc curve and neural networks |
| url | http://hdl.handle.net/20.500.11939/5544 |
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