About identification of features that affect the estimation of citrus harvest
Accurate models for early harvest estimation in citrus production generally involve expensive variables. The goal of this research work was to develop a model to provide early and accurate estimations of harvest using low-cost features. Given the original data may derive from tree measurements, met...
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Facultad de Ciencias Agrarias-UNCuyo
2023
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| Acceso en línea: | https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/5452 |
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I11-R107article-54522023-09-14T18:41:42Z About identification of features that affect the estimation of citrus harvest About identification of features that affect the estimation of citrus harvest Bóbeda, Griselda R. R. Mazza, Silvia M. Rico , Noelia Brenes Pérez, Cristian F. Gaiad, José E. Díaz Rodríguez, Susana Irene MODIS SVM selección de variables aprendizaje automático naranja dulce tangor Murcott MODIS SVM selection of variables machine learning sweet orange Murcott tangor Accurate models for early harvest estimation in citrus production generally involve expensive variables. The goal of this research work was to develop a model to provide early and accurate estimations of harvest using low-cost features. Given the original data may derive from tree measurements, meteorological stations, or satellites, they have varied costs. The studied orchards included tangerines (Citrus reticulata x C. sinensis) and sweet oranges (C. sinensis) located in northeastern Argentina. Machine learning methods combined with different datasets were tested to obtain the most accurate harvest estimation. The final model is based on support vector machines with low-cost variables like species, age, irrigation, red and near-infrared reflectance in February and December, NDVI in December, rain during ripening, and humidity during fruit growth. Highlights: Red and near-infrared reflectance in February and December are helpful values to predict orange harvest. SVM is an efficient method to predict harvest. A ranking method to A ranking-based method has been developed to identify the variables that best predict orange production. Accurate models for early harvest estimation in citrus production generally involve expensive variables. The goal of this research work was to develop a model to provide early and accurate estimations of harvest using low-cost features. Given the original data may derive from tree measurements, meteorological stations, or satellites, they have varied costs. The studied orchards included tangerines (Citrus reticulata x C. sinensis) and sweet oranges (C. sinensis) located in northeastern Argentina. Machine learning methods combined with different datasets were tested to obtain the most accurate harvest estimation. The final model is based on support vector machines with low-cost variables like species, age, irrigation, red and near-infrared reflectance in February and December, NDVI in December, rain during ripening, and humidity during fruit growth. Highlights: Red and near-infrared reflectance in February and December are helpful values to predict orange harvest. SVM is an efficient method to predict harvest. A ranking method to A ranking-based method has been developed to identify the variables that best predict orange production. Facultad de Ciencias Agrarias-UNCuyo 2023-06-27 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/5452 10.48162/rev.39.096 Revista de la Facultad de Ciencias Agrarias UNCuyo; Vol. 55 No. 1 (2023): January-June; 65-74 Revista de la Facultad de Ciencias Agrarias UNCuyo; Vol. 55 Núm. 1 (2023): Enero-Junio; 65-74 1853-8665 0370-4661 eng https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/5452/5568 Derechos de autor 2018 Revista de la Facultad de Ciencias Agrarias UNCuyo https://creativecommons.org/licenses/by-nc-sa/3.0/deed.es |
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Universidad Nacional de Cuyo |
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Revistas en línea |
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Revistas en línea |
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Revista de la Facultad de Ciencias Agrarias |
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Revista de la Facultad de Ciencias Agrarias |
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I-11 |
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R-107 |
| language |
eng |
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Online |
| author |
Bóbeda, Griselda R. R. Mazza, Silvia M. Rico , Noelia Brenes Pérez, Cristian F. Gaiad, José E. Díaz Rodríguez, Susana Irene |
| spellingShingle |
Bóbeda, Griselda R. R. Mazza, Silvia M. Rico , Noelia Brenes Pérez, Cristian F. Gaiad, José E. Díaz Rodríguez, Susana Irene About identification of features that affect the estimation of citrus harvest MODIS SVM selección de variables aprendizaje automático naranja dulce tangor Murcott MODIS SVM selection of variables machine learning sweet orange Murcott tangor |
| author_facet |
Bóbeda, Griselda R. R. Mazza, Silvia M. Rico , Noelia Brenes Pérez, Cristian F. Gaiad, José E. Díaz Rodríguez, Susana Irene |
| author_sort |
Bóbeda, Griselda R. R. |
| title |
About identification of features that affect the estimation of citrus harvest |
| title_short |
About identification of features that affect the estimation of citrus harvest |
| title_full |
About identification of features that affect the estimation of citrus harvest |
| title_fullStr |
About identification of features that affect the estimation of citrus harvest |
| title_full_unstemmed |
About identification of features that affect the estimation of citrus harvest |
| title_sort |
about identification of features that affect the estimation of citrus harvest |
| description |
Accurate models for early harvest estimation in citrus production generally involve expensive variables. The goal of this research work was to develop a model to provide early and accurate estimations of harvest using low-cost features. Given the original data may derive from tree measurements, meteorological stations, or satellites, they have varied costs. The studied orchards included tangerines (Citrus reticulata x C. sinensis) and sweet oranges (C. sinensis) located in northeastern Argentina. Machine learning methods combined with different datasets were tested to obtain the most accurate harvest estimation. The final model is based on support vector machines with low-cost variables like species, age, irrigation, red and near-infrared reflectance in February and December, NDVI in December, rain during ripening, and humidity during fruit growth.
Highlights:
Red and near-infrared reflectance in February and December are helpful values to predict orange harvest.
SVM is an efficient method to predict harvest.
A ranking method to A ranking-based method has been developed to identify the variables that best predict orange production.
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| publisher |
Facultad de Ciencias Agrarias-UNCuyo |
| publishDate |
2023 |
| url |
https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/5452 |
| topic |
MODIS SVM selección de variables aprendizaje automático naranja dulce tangor Murcott MODIS SVM selection of variables machine learning sweet orange Murcott tangor |
| topic_facet |
MODIS SVM selección de variables aprendizaje automático naranja dulce tangor Murcott MODIS SVM selection of variables machine learning sweet orange Murcott tangor |
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