Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging
Blackheart is one of the primary diseases affecting pomegranate fruit globally, caused by the fungus Alternaria. The damages are not visually detectable, as it is an internal disease that requires non-invasive technologies to provide information from inside the fruit to be detected. This study e...
| Autores principales: | , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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Elsevier
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.11939/9022 https://www.sciencedirect.com/science/article/pii/S0023643824015317 |
| _version_ | 1855492608891027456 |
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| author | Munera, Sandra Rodríguez-Ortega, Alejandro Cubero, Sergio Aleixos, Nuria Blasco, José |
| author_browse | Aleixos, Nuria Blasco, José Cubero, Sergio Munera, Sandra Rodríguez-Ortega, Alejandro |
| author_facet | Munera, Sandra Rodríguez-Ortega, Alejandro Cubero, Sergio Aleixos, Nuria Blasco, José |
| author_sort | Munera, Sandra |
| collection | ReDivia |
| description | Blackheart is one of the primary diseases affecting pomegranate fruit globally, caused by the fungus Alternaria.
The damages are not visually detectable, as it is an internal disease that requires non-invasive technologies to
provide information from inside the fruit to be detected. This study explored the ability of X-ray imaging to
detect this infection in ‘Wonderful’ pomegranate fruit. X-ray images of healthy and infected fruit at different
levels were acquired and analysed. Texture features based on first-order statistics, the grey-level co-occurrence
matrix (GLCM), and grey-level histograms with several resolutions were extracted from X-ray images and used to
classify the fruit as healthy or infected through the random forest algorithm. The presence of the infection in
three levels of severity was later assessed by destructive visual analysis by opening the samples in half. The
highest accuracy models were obtained using all texture features and histograms with 256 bins. Compared to
manual inspection, X-rays showed a clear advantage in detecting incipient infections (infected fruit at level 1),
correctly identifying 93.3 % of infected fruits. In contrast, the manual inspection identified only 66.7 % of fruit,
highlighting the limitations of early-stage detection. |
| format | Artículo |
| id | ReDivia9022 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | ReDivia90222025-04-25T14:49:47Z Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging Munera, Sandra Rodríguez-Ortega, Alejandro Cubero, Sergio Aleixos, Nuria Blasco, José Heart rot Texture features Histogram X-ray Machine learning Blackheart is one of the primary diseases affecting pomegranate fruit globally, caused by the fungus Alternaria. The damages are not visually detectable, as it is an internal disease that requires non-invasive technologies to provide information from inside the fruit to be detected. This study explored the ability of X-ray imaging to detect this infection in ‘Wonderful’ pomegranate fruit. X-ray images of healthy and infected fruit at different levels were acquired and analysed. Texture features based on first-order statistics, the grey-level co-occurrence matrix (GLCM), and grey-level histograms with several resolutions were extracted from X-ray images and used to classify the fruit as healthy or infected through the random forest algorithm. The presence of the infection in three levels of severity was later assessed by destructive visual analysis by opening the samples in half. The highest accuracy models were obtained using all texture features and histograms with 256 bins. Compared to manual inspection, X-rays showed a clear advantage in detecting incipient infections (infected fruit at level 1), correctly identifying 93.3 % of infected fruits. In contrast, the manual inspection identified only 66.7 % of fruit, highlighting the limitations of early-stage detection. 2025-02-11T12:36:49Z 2025-02-11T12:36:49Z 2025 article publishedVersion Munera, S., Rodríguez-Ortega, A., Cubero, S., Aleixos, N., & Blasco, J. (2025). Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging. LWT, 215, 117248. 0023-6438 https://hdl.handle.net/20.500.11939/9022 10.1016/j.lwt.2024.117248 https://www.sciencedirect.com/science/article/pii/S0023643824015317 en This work was partially funded by the projects GVA-PROMETEO CIPROM/2021/014, MICIU-AEI (PID2023-150192OR-C31 and C32) and GVA-IVIA 52204 with the EU through the European Regional Development Fund (ERDF) from GVA 2021–2027. Sandra Munera thanks the postdoctoral contract Juan de la Cierva-Formacion ´ (FJC2021-047786-I) co-funded by MCIN/AEI/10.13039/ 501100011033 and European Union NextGenerationEU/PRTR 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 info:eu-repo/grantAgreement/AEI/Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia/PID2023-150192OR-C31/Automatización de la inspección de la calidad interna y seguridad de frutas en linea, utilizando espectroscopia VIS-NIR e inteligencia artificial info:eu-repo/grantAgreement/AEI/Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia/PID2023-150192OR-C32/Automatización de la inspección de la calidad interna y seguridad de frutas en linea, utilizando imagen hiperspectral VIS-NIR e inteligencia artificial Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess Elsevier electronico |
| spellingShingle | Heart rot Texture features Histogram X-ray Machine learning Munera, Sandra Rodríguez-Ortega, Alejandro Cubero, Sergio Aleixos, Nuria Blasco, José Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging |
| title | Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging |
| title_full | Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging |
| title_fullStr | Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging |
| title_full_unstemmed | Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging |
| title_short | Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging |
| title_sort | automatic detection of pomegranate fruit affected by blackheart disease using x ray imaging |
| topic | Heart rot Texture features Histogram X-ray Machine learning |
| url | https://hdl.handle.net/20.500.11939/9022 https://www.sciencedirect.com/science/article/pii/S0023643824015317 |
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