Tracing pistachio nuts’ origin and irrigation practices through hyperspectral imaging
Pistachio trees have become a significant global agricultural commodity because their nuts are renowned for their unique flavour and numerous health benefits, contributing to their high demand worldwide. This study explores the application of Hyperspectral Imaging (HSI) and Machine Learning (ML) to...
| Autores principales: | , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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Elsevier
2024
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| Acceso en línea: | https://hdl.handle.net/20.500.11939/8991 https://www.sciencedirect.com/science/article/pii/S2665927124001618 |
| _version_ | 1855492602584891392 |
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| author | Martínez-Peña, Raquel Castillo-Gironés, Salvador Álvarez, Sara Vélez, Sergio |
| author_browse | Castillo-Gironés, Salvador Martínez-Peña, Raquel Vélez, Sergio Álvarez, Sara |
| author_facet | Martínez-Peña, Raquel Castillo-Gironés, Salvador Álvarez, Sara Vélez, Sergio |
| author_sort | Martínez-Peña, Raquel |
| collection | ReDivia |
| description | Pistachio trees have become a significant global agricultural commodity because their nuts are renowned for their unique flavour and numerous health benefits, contributing to their high demand worldwide. This study explores the application of Hyperspectral Imaging (HSI) and Machine Learning (ML) to determine pistachio nuts' geographic origin and irrigation practices, alongside predicting essential commercial quality and yield parameters. The study was conducted in two Spanish orchards and employed HSI technology to capture spectral data. It used ML models like Partial Least Squares (PLS), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for analysis.
The results demonstrated high accuracy in classifying pistachios based on origin, with accuracies exceeding 94%, and in assessing water content and colour pigments, where both PLS and SVM models achieved 99% accuracy. The research highlighted distinct spectral signatures associated with different irrigation treatments, particularly in the Near-Infrared (NIR) region, with PLS showing an accuracy of 92%. However, challenges were noted in predicting fruit orientation, while predicting height location within the tree was more successful, reflecting clearer spectral distinctions. Regression models also showed promise, particularly in predicting yield (R2 = 0.89 with PLS) and percentage of blank nuts (R2 = 0.71 with PLS). The correlation analysis revealed key insights, such as an inverse relationship between blank nuts and yield, and a strong correlation between yield and split nuts. Despite challenges in predicting fruit orientation, the research showed promising results in forecasting yield and commercial quality factors, indicating the effectiveness of spectral analysis in optimising pistachio production and sustainability. |
| format | Artículo |
| id | ReDivia8991 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | ReDivia89912025-04-25T14:49:45Z Tracing pistachio nuts’ origin and irrigation practices through hyperspectral imaging Martínez-Peña, Raquel Castillo-Gironés, Salvador Álvarez, Sara Vélez, Sergio Hyperspectral imaging Irrigation treatments Q Food science Pistacia vera traceability Pistachio trees have become a significant global agricultural commodity because their nuts are renowned for their unique flavour and numerous health benefits, contributing to their high demand worldwide. This study explores the application of Hyperspectral Imaging (HSI) and Machine Learning (ML) to determine pistachio nuts' geographic origin and irrigation practices, alongside predicting essential commercial quality and yield parameters. The study was conducted in two Spanish orchards and employed HSI technology to capture spectral data. It used ML models like Partial Least Squares (PLS), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for analysis. The results demonstrated high accuracy in classifying pistachios based on origin, with accuracies exceeding 94%, and in assessing water content and colour pigments, where both PLS and SVM models achieved 99% accuracy. The research highlighted distinct spectral signatures associated with different irrigation treatments, particularly in the Near-Infrared (NIR) region, with PLS showing an accuracy of 92%. However, challenges were noted in predicting fruit orientation, while predicting height location within the tree was more successful, reflecting clearer spectral distinctions. Regression models also showed promise, particularly in predicting yield (R2 = 0.89 with PLS) and percentage of blank nuts (R2 = 0.71 with PLS). The correlation analysis revealed key insights, such as an inverse relationship between blank nuts and yield, and a strong correlation between yield and split nuts. Despite challenges in predicting fruit orientation, the research showed promising results in forecasting yield and commercial quality factors, indicating the effectiveness of spectral analysis in optimising pistachio production and sustainability. 2024-10-07T11:31:38Z 2024-10-07T11:31:38Z 2024 article publishedVersion Martínez-Peña, R., Castillo-Gironés, S., Álvarez, S., & Vélez, S. (2024). Tracing Pistachio Nuts’ Origin and Irrigation Practices through Hyperspectral Imaging. Current Research in Food Science, 100835. https://hdl.handle.net/20.500.11939/8991 10.1016/j.crfs.2024.100835 https://www.sciencedirect.com/science/article/pii/S2665927124001618 en Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess Elsevier electronico |
| spellingShingle | Hyperspectral imaging Irrigation treatments Q Food science Pistacia vera traceability Martínez-Peña, Raquel Castillo-Gironés, Salvador Álvarez, Sara Vélez, Sergio Tracing pistachio nuts’ origin and irrigation practices through hyperspectral imaging |
| title | Tracing pistachio nuts’ origin and irrigation practices through hyperspectral imaging |
| title_full | Tracing pistachio nuts’ origin and irrigation practices through hyperspectral imaging |
| title_fullStr | Tracing pistachio nuts’ origin and irrigation practices through hyperspectral imaging |
| title_full_unstemmed | Tracing pistachio nuts’ origin and irrigation practices through hyperspectral imaging |
| title_short | Tracing pistachio nuts’ origin and irrigation practices through hyperspectral imaging |
| title_sort | tracing pistachio nuts origin and irrigation practices through hyperspectral imaging |
| topic | Hyperspectral imaging Irrigation treatments Q Food science Pistacia vera traceability |
| url | https://hdl.handle.net/20.500.11939/8991 https://www.sciencedirect.com/science/article/pii/S2665927124001618 |
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