Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins
Nowadays, the detection of fruit infected with Penicillium sp. fungi on packing lines is carried out manually under ultraviolet illumination. Ultraviolet sources induce visible fluorescence of essential oils present in the skin of citrus and which are released by the action of fungi, thus increasing...
| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/5315 |
| _version_ | 1855032259727327232 |
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| author | Gómez-Sanchís, Juan Gomez-Chova, L. Aleixos, Nuria Camps-Valls, G. Montesinos-Herrero, Clara Moltó, Enrique Blasco, José |
| author_browse | Aleixos, Nuria Blasco, José Camps-Valls, G. Gomez-Chova, L. Gómez-Sanchís, Juan Moltó, Enrique Montesinos-Herrero, Clara |
| author_facet | Gómez-Sanchís, Juan Gomez-Chova, L. Aleixos, Nuria Camps-Valls, G. Montesinos-Herrero, Clara Moltó, Enrique Blasco, José |
| author_sort | Gómez-Sanchís, Juan |
| collection | ReDivia |
| description | Nowadays, the detection of fruit infected with Penicillium sp. fungi on packing lines is carried out manually under ultraviolet illumination. Ultraviolet sources induce visible fluorescence of essential oils present in the skin of citrus and which are released by the action of fungi, thus increasing the contrast between sound and rotten skin. This work analyses a set of techniques aimed at detecting rotten citrus without the use of UV lighting. The techniques used include hyperspectral image acquisition, preprocessing and calibration, feature selection and segmentation using linear and non-linear methods for classification of fruits. Different methods such as correlation analysis, mutual information, stepwise, and genetic algorithms based on linear discriminant analysis (LDA) are studied to select the most relevant bands. image segmentation relies on the combination of efficient band selection techniques and also on pixel classification methods such as classification and regression trees (CART) and LDA. The results were obtained using a large dataset of images of mandarins cv. "Clemenules" by applying the CART method. The hyperspectral computer vision system proposed here is capable of detecting damage caused by Penicillium digitatum in mandarins using a reduced set of optimally selected bands. |
| format | article |
| id | ReDivia5315 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | ReDivia53152025-04-25T14:42:01Z Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins Gómez-Sanchís, Juan Gomez-Chova, L. Aleixos, Nuria Camps-Valls, G. Montesinos-Herrero, Clara Moltó, Enrique Blasco, José Nowadays, the detection of fruit infected with Penicillium sp. fungi on packing lines is carried out manually under ultraviolet illumination. Ultraviolet sources induce visible fluorescence of essential oils present in the skin of citrus and which are released by the action of fungi, thus increasing the contrast between sound and rotten skin. This work analyses a set of techniques aimed at detecting rotten citrus without the use of UV lighting. The techniques used include hyperspectral image acquisition, preprocessing and calibration, feature selection and segmentation using linear and non-linear methods for classification of fruits. Different methods such as correlation analysis, mutual information, stepwise, and genetic algorithms based on linear discriminant analysis (LDA) are studied to select the most relevant bands. image segmentation relies on the combination of efficient band selection techniques and also on pixel classification methods such as classification and regression trees (CART) and LDA. The results were obtained using a large dataset of images of mandarins cv. "Clemenules" by applying the CART method. The hyperspectral computer vision system proposed here is capable of detecting damage caused by Penicillium digitatum in mandarins using a reduced set of optimally selected bands. 2017-06-01T10:12:07Z 2017-06-01T10:12:07Z 2008 NOV 2008 article Gomez-Sanchis, J., Gomez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., Blasco, J. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80-86. 0260-8774 http://hdl.handle.net/20.500.11939/5315 10.1016/j.jfoodeng.2008.04.009 en openAccess Impreso |
| spellingShingle | Gómez-Sanchís, Juan Gomez-Chova, L. Aleixos, Nuria Camps-Valls, G. Montesinos-Herrero, Clara Moltó, Enrique Blasco, José Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins |
| title | Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins |
| title_full | Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins |
| title_fullStr | Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins |
| title_full_unstemmed | Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins |
| title_short | Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins |
| title_sort | hyperspectral system for early detection of rottenness caused by penicillium digitatum in mandarins |
| url | http://hdl.handle.net/20.500.11939/5315 |
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