New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis
Anthracnose is one of the most relevant diseases of mango crops in producing regions, affecting 60% of production. Currently, its detection is carried out in late stages by human visual inspection. Hyperspectral imaging systems allow the development of non-destructive solutions to inspect and detect...
| Main Authors: | , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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Springer
2023
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| Online Access: | https://hdl.handle.net/20.500.11939/8743 https://link.springer.com/article/10.1007/s11694-023-02173-3 |
| _version_ | 1855492550868074496 |
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| author | Velásquez, Carlos Prieto, Flavio Palou, Lluís Cubero, Sergio Blasco, José Aleixos, Nuria |
| author_browse | Aleixos, Nuria Blasco, José Cubero, Sergio Palou, Lluís Prieto, Flavio Velásquez, Carlos |
| author_facet | Velásquez, Carlos Prieto, Flavio Palou, Lluís Cubero, Sergio Blasco, José Aleixos, Nuria |
| author_sort | Velásquez, Carlos |
| collection | ReDivia |
| description | Anthracnose is one of the most relevant diseases of mango crops in producing regions, affecting 60% of production. Currently, its detection is carried out in late stages by human visual inspection. Hyperspectral imaging systems allow the development of non-destructive solutions to inspect and detect internal damage. This work aimed to develop a system for detecting anthracnose in mango fruits using Vis-NIR hyperspectral imaging and discriminant analysis. The usefulness of three-dimensionality reduction methods to minimise redundancy in the spectral data and to obtain a compact number of wavelengths that effectively allow the detection of anthracnose symptoms in mango fruits is also explored. As a result, a classification model based on discriminant analysis and Pearson correlation coefficient was obtained, showing the potential of hyperspectral data to robustly allow the detection of anthracnose symptoms with full or reduced spectra. The findings reported in this study can serve as the basis for developing an anthracnose detection system in mango fruits with multispectral cameras |
| format | Artículo |
| id | ReDivia8743 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | ReDivia87432025-04-25T14:49:25Z New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis Velásquez, Carlos Prieto, Flavio Palou, Lluís Cubero, Sergio Blasco, José Aleixos, Nuria Anthracnose disease Automatic inspection Fruit quality Vis-NIR hyperspectral imaging H20 Plant diseases N20 Agricultural machinery and equipment U30 Research methods Mangifera indica Image analysis Disease symptoms Anthracnose is one of the most relevant diseases of mango crops in producing regions, affecting 60% of production. Currently, its detection is carried out in late stages by human visual inspection. Hyperspectral imaging systems allow the development of non-destructive solutions to inspect and detect internal damage. This work aimed to develop a system for detecting anthracnose in mango fruits using Vis-NIR hyperspectral imaging and discriminant analysis. The usefulness of three-dimensionality reduction methods to minimise redundancy in the spectral data and to obtain a compact number of wavelengths that effectively allow the detection of anthracnose symptoms in mango fruits is also explored. As a result, a classification model based on discriminant analysis and Pearson correlation coefficient was obtained, showing the potential of hyperspectral data to robustly allow the detection of anthracnose symptoms with full or reduced spectra. The findings reported in this study can serve as the basis for developing an anthracnose detection system in mango fruits with multispectral cameras 2023-11-21T08:53:22Z 2023-11-21T08:53:22Z 2024 article publishedVersion Velásquez, C., Prieto, F., Palou, L., Cubero, S., Blasco, J., & Aleixos, N. (2024). New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis. Journal of Food Measurement and Characterization, 18(1), 560-570. 2193-4126 (Print ISSN) 2193-4134 (Electronic ISSN) https://hdl.handle.net/20.500.11939/8743 10.1007/s11694-023-02173-3 https://link.springer.com/article/10.1007/s11694-023-02173-3 en This work was partially funded by the Ministerio de ciencia y tecnología de Colombia (MINCIENCIAS) through its call “convocatoria 785 para doctorados nacionales 2017”, Universidad Nacional de Colombia through its programme “convocatoria para el apoyo a proyectos de investigación y creación artística de la sede Bogotá de la Universidad Nacional de Colombia-2019” and the Sistema General de Regalías CTeI-Colombia (BPIN 2020000100415, “Desarrollo de un sistema de óptico computacional para estimar el contenido de carbono orgánico de suelos agrícolas a través de imágenes espectrales e inteligencia artificial en cultivos cítricos de Santander”, code UIS-8933) and through GVA-IVIA 52204 and GVA-PROMETEO CIPROM/2021/014. info:eu-repo/grantAgreement/ERDF/PCV 2021-2027/52204/ES/Tecnología inteligente para una agricultura digital, sostenible y precisa en la comunitat valenciana/AgrIntel·ligència-CV Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess Springer electronico |
| spellingShingle | Anthracnose disease Automatic inspection Fruit quality Vis-NIR hyperspectral imaging H20 Plant diseases N20 Agricultural machinery and equipment U30 Research methods Mangifera indica Image analysis Disease symptoms Velásquez, Carlos Prieto, Flavio Palou, Lluís Cubero, Sergio Blasco, José Aleixos, Nuria New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis |
| title | New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis |
| title_full | New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis |
| title_fullStr | New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis |
| title_full_unstemmed | New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis |
| title_short | New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis |
| title_sort | new model for the automatic detection of anthracnose in mango fruits based on vis nir hyperspectral imaging and discriminant analysis |
| topic | Anthracnose disease Automatic inspection Fruit quality Vis-NIR hyperspectral imaging H20 Plant diseases N20 Agricultural machinery and equipment U30 Research methods Mangifera indica Image analysis Disease symptoms |
| url | https://hdl.handle.net/20.500.11939/8743 https://link.springer.com/article/10.1007/s11694-023-02173-3 |
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