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, 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.
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| Main Authors: | , |
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| Format: | Artículo biblioteca |
| Language: | English |
| Published: |
2023-06-27
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| Subjects: | Citrus reticulata, Naranja dulce, sweet oranges, laranja, orange douce, Aprendizaje automático, machine learning, aprendizagem electrónica, apprentissage machine, Tangor, tangors, tangor, Sede Central, ODS 12 - Producción y consumo responsables, |
| Online Access: | https://repositorio.catie.ac.cr/handle/11554/12239 |
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dig-catie-11554-122392023-08-15T14:36:35Z About identification of features that affect the estimation of citrus harvest Brenes Pérez, Cristian F. y 5 autores más Citrus reticulata Citrus reticulata Citrus reticulata Citrus reticulata Naranja dulce sweet oranges laranja orange douce Aprendizaje automático machine learning aprendizagem electrónica apprentissage machine Tangor tangors tangor tangor Sede Central ODS 12 - Producción y consumo responsables 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. En la producción de cítricos, los modelos precisos para estimación temprana de producción involucran variables de alto costo. El objetivo de este trabajo fue desarrollar un modelo que proporcione estimaciones tempranas y precisas utilizando características de bajo costo. Los datos iniciales considerados tienen diferentes costos, ya que provienen de mediciones en los árboles, de las estaciones meteorológicas o de satélite. Los huertos de cítricos estudiados correspondieron a mandarino (Citrus reticulata x C. sinensis) y dos naranjas dulces (C. sinensis); ubicados en el noreste argentino. Se han probado varios métodos de aprendizaje automático junto con diferentes conjuntos de datos, con el objetivo de obtener la mejor estimación de producción. El modelo final se basa en máquinas de vectores soporte con las siguientes variables de bajo costo: especie, edad de los árboles, irrigación, reflectancia roja e infrarroja cercana en febrero y diciembre, NDVI en diciembre, lluvia durante madurez y humedad en periodo de crecimiento de frutos. 2023-07-11T15:34:04Z 2023-07-11T15:34:04Z 2023-06-27 Artículo https://repositorio.catie.ac.cr/handle/11554/12239 openAccess en Revista de la Facultad de Ciencias Agrarias UNCuyo https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/5452 application/pdf |
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| countrycode |
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| component |
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| access |
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| databasecode |
dig-catie |
| tag |
biblioteca |
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Biblioteca Conmemorativa Orton |
| language |
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| topic |
Citrus reticulata Citrus reticulata Citrus reticulata Citrus reticulata Naranja dulce sweet oranges laranja orange douce Aprendizaje automático machine learning aprendizagem electrónica apprentissage machine Tangor tangors tangor tangor Sede Central ODS 12 - Producción y consumo responsables Citrus reticulata Citrus reticulata Citrus reticulata Citrus reticulata Naranja dulce sweet oranges laranja orange douce Aprendizaje automático machine learning aprendizagem electrónica apprentissage machine Tangor tangors tangor tangor Sede Central ODS 12 - Producción y consumo responsables |
| spellingShingle |
Citrus reticulata Citrus reticulata Citrus reticulata Citrus reticulata Naranja dulce sweet oranges laranja orange douce Aprendizaje automático machine learning aprendizagem electrónica apprentissage machine Tangor tangors tangor tangor Sede Central ODS 12 - Producción y consumo responsables Citrus reticulata Citrus reticulata Citrus reticulata Citrus reticulata Naranja dulce sweet oranges laranja orange douce Aprendizaje automático machine learning aprendizagem electrónica apprentissage machine Tangor tangors tangor tangor Sede Central ODS 12 - Producción y consumo responsables Brenes Pérez, Cristian F. y 5 autores más 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. |
| format |
Artículo |
| topic_facet |
Citrus reticulata Citrus reticulata Citrus reticulata Citrus reticulata Naranja dulce sweet oranges laranja orange douce Aprendizaje automático machine learning aprendizagem electrónica apprentissage machine Tangor tangors tangor tangor Sede Central ODS 12 - Producción y consumo responsables |
| author |
Brenes Pérez, Cristian F. y 5 autores más |
| author_facet |
Brenes Pérez, Cristian F. y 5 autores más |
| author_sort |
Brenes Pérez, Cristian F. |
| 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 |
| publishDate |
2023-06-27 |
| url |
https://repositorio.catie.ac.cr/handle/11554/12239 |
| work_keys_str_mv |
AT brenesperezcristianf aboutidentificationoffeaturesthataffecttheestimationofcitrusharvest AT y5autoresmas aboutidentificationoffeaturesthataffecttheestimationofcitrusharvest |
| _version_ |
1774852461581828096 |