Estimation of potato yield using satellite data at a municipal level: A machine learning approach

Producción Científica

Detalles Bibliográficos
Autores: Salvador González, Pablo, Gómez, Diego, Sanz Justo, María Julia, Casanova Roque, José Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/59075
Acceso en línea:https://doi.org/10.3390/ijgi9060343
https://uvadoc.uva.es/handle/10324/59075
Access Level:acceso abierto
Palabra clave:Física
Teledetección
Potato yield
Meteorological data
Satellite imagery
Municipal level
Rendimiento de patatas
Datos meteorológicos
Imágenes de satélite
Ámbito municipal
22 Física
3103.04 Protección de Los Cultivos
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oai_identifier_str oai:uvadoc.uva.es:10324/59075
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spelling Estimation of potato yield using satellite data at a municipal level: A machine learning approachSalvador González, PabloGómez, DiegoSanz Justo, María JuliaCasanova Roque, José LuisFísicaTeledetecciónPotato yieldMeteorological dataSatellite imageryMunicipal levelRendimiento de patatasDatos meteorológicosImágenes de satéliteÁmbito municipal22 Física3103.04 Protección de Los CultivosProducción CientíficaCrop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) dataset developed by the Copernicus Climate Change Service, satellite imagery from the TERRA platform, and field information. Five different machine learning algorithms were used to build the models: random forest (rf), support vector machine linear (svmL), support vector machine polynomial (svmP), support vector machine radial (svmR), and general linear model (glm). The optimized models were tested using independent data (2017 and 2018) not used in the training and optimization phase (2004–2016). In terms of percent root mean squared error (%RMSE), the best results were obtained by the rf algorithm in the winter cycle using variables from the first three months of the cycle (R2 = 0.757 and %RMSE = 18.9). For the summer cycle, the best performing model was the svmP which used the first five months of the cycle as variables (R2 = 0.858 and %RMSE = 14.9). Our results indicated that adding predictor variables of the last two months before the harvest did not significantly improved model performances. These results demonstrate that our models can predict potato yield by analyzing the yield of the previous year, the general conditions of NDVI, meteorology, and information related to the irrigation system at a municipal level.MDPI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3390/ijgi9060343https://uvadoc.uva.es/handle/10324/59075reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://www.mdpi.com/2220-9964/9/6/343info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:uvadoc.uva.es:10324/590752026-06-13T12:44:47Z
dc.title.none.fl_str_mv Estimation of potato yield using satellite data at a municipal level: A machine learning approach
title Estimation of potato yield using satellite data at a municipal level: A machine learning approach
spellingShingle Estimation of potato yield using satellite data at a municipal level: A machine learning approach
Salvador González, Pablo
Física
Teledetección
Potato yield
Meteorological data
Satellite imagery
Municipal level
Rendimiento de patatas
Datos meteorológicos
Imágenes de satélite
Ámbito municipal
22 Física
3103.04 Protección de Los Cultivos
title_short Estimation of potato yield using satellite data at a municipal level: A machine learning approach
title_full Estimation of potato yield using satellite data at a municipal level: A machine learning approach
title_fullStr Estimation of potato yield using satellite data at a municipal level: A machine learning approach
title_full_unstemmed Estimation of potato yield using satellite data at a municipal level: A machine learning approach
title_sort Estimation of potato yield using satellite data at a municipal level: A machine learning approach
dc.creator.none.fl_str_mv Salvador González, Pablo
Gómez, Diego
Sanz Justo, María Julia
Casanova Roque, José Luis
author Salvador González, Pablo
author_facet Salvador González, Pablo
Gómez, Diego
Sanz Justo, María Julia
Casanova Roque, José Luis
author_role author
author2 Gómez, Diego
Sanz Justo, María Julia
Casanova Roque, José Luis
author2_role author
author
author
dc.subject.none.fl_str_mv Física
Teledetección
Potato yield
Meteorological data
Satellite imagery
Municipal level
Rendimiento de patatas
Datos meteorológicos
Imágenes de satélite
Ámbito municipal
22 Física
3103.04 Protección de Los Cultivos
topic Física
Teledetección
Potato yield
Meteorological data
Satellite imagery
Municipal level
Rendimiento de patatas
Datos meteorológicos
Imágenes de satélite
Ámbito municipal
22 Física
3103.04 Protección de Los Cultivos
description Producción Científica
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/ijgi9060343
https://uvadoc.uva.es/handle/10324/59075
url https://doi.org/10.3390/ijgi9060343
https://uvadoc.uva.es/handle/10324/59075
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2220-9964/9/6/343
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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