Machine learning applied to the prediction of citrus production

An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniq...

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Autores principales: Díaz, Irene, Mazza, Silvia Matilde, Combarro, Elías F., Giménez, Laura Itatí, Gaiad, José Emilio
Formato: Artículo
Lenguaje:Inglés
Publicado: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria 2022
Materias:
Lemon
Mandarin
Orange
M5-Prime
Age
Framework
Irrigation
Acceso en línea:http://repositorio.unne.edu.ar/handle/123456789/30845
Aporte de:
RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) de Universidad Nacional del Nordeste
id I48-R184-123456789-30845
record_format dspace
institution Universidad Nacional del Nordeste
institution_str I-48
repository_str R-184
collection RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
language Inglés
topic Lemon
Mandarin
Orange
M5-Prime
Age
Framework
Irrigation
spellingShingle Lemon
Mandarin
Orange
M5-Prime
Age
Framework
Irrigation
Díaz, Irene
Mazza, Silvia Matilde
Combarro, Elías F.
Giménez, Laura Itatí
Gaiad, José Emilio
Machine learning applied to the prediction of citrus production
topic_facet Lemon
Mandarin
Orange
M5-Prime
Age
Framework
Irrigation
description An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees’ age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.
format Artículo
author Díaz, Irene
Mazza, Silvia Matilde
Combarro, Elías F.
Giménez, Laura Itatí
Gaiad, José Emilio
author_facet Díaz, Irene
Mazza, Silvia Matilde
Combarro, Elías F.
Giménez, Laura Itatí
Gaiad, José Emilio
author_sort Díaz, Irene
title Machine learning applied to the prediction of citrus production
title_short Machine learning applied to the prediction of citrus production
title_full Machine learning applied to the prediction of citrus production
title_fullStr Machine learning applied to the prediction of citrus production
title_full_unstemmed Machine learning applied to the prediction of citrus production
title_sort machine learning applied to the prediction of citrus production
publisher Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
publishDate 2022
url http://repositorio.unne.edu.ar/handle/123456789/30845
work_keys_str_mv AT diazirene machinelearningappliedtothepredictionofcitrusproduction
AT mazzasilviamatilde machinelearningappliedtothepredictionofcitrusproduction
AT combarroeliasf machinelearningappliedtothepredictionofcitrusproduction
AT gimenezlauraitati machinelearningappliedtothepredictionofcitrusproduction
AT gaiadjoseemilio machinelearningappliedtothepredictionofcitrusproduction
bdutipo_str Repositorios
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