Using NDVI, climate data and machine learning to estimate yield in the Douro wine region

Detalhes bibliográficos
Autor(a) principal: Barriguinha, André
Data de Publicação: 2022
Outros Autores: Jardim, Bruno, Neto, Miguel de Castro, Gil, Artur José Freire
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.3/6479
Resumo: The authors gratefully acknowledge: IVDP - Instituto dos Vinhos do Douro e do Porto, IP (Institute of Douro and Port Wines) (https://www.ivdp.pt/en), for providing historical data related to wine grape production for the entire DDR at the parish level; IPMA - Instituto Português do Mar e da Atmosfera, IP (Portuguese Institute for Sea and Atmosphere)
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spelling Using NDVI, climate data and machine learning to estimate yield in the Douro wine regionRemote SensingVineyardYieldEstimationPredictionClimateMachine LearningNDVIThe authors gratefully acknowledge: IVDP - Instituto dos Vinhos do Douro e do Porto, IP (Institute of Douro and Port Wines) (https://www.ivdp.pt/en), for providing historical data related to wine grape production for the entire DDR at the parish level; IPMA - Instituto Português do Mar e da Atmosfera, IP (Portuguese Institute for Sea and Atmosphere)Estimating vineyard yield in advance is essential for planning and regulatory purposes at the regional level, with growing importance in a long-term scenario of perceived climate change. With few tools available, the current study aimed to develop a yield estimation model based on remote sensing and climate data with a machine-learning approach. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The study was conducted in the Douro Demarcated Region in Portugal over the period 2016–2021 using yield data from 169 administrative areas that cover 250,000 ha, in which 43,000 ha of the vineyard are in production. The optimal combination of input features, with an Mean Absolute Error (MAE) of 672.55 kg/ha and an Mean Squared Error (MSE) of 81.30 kg/ha, included the NDVI, Temperature, Relative Humidity, Precipitation, and Wind Intensity. The model was tested for each year, using it as the test set, while all other years were used as input to train the model. Two different moments in time, corresponding to FLO (flowering) and VER (veraison), were considered to estimate in advance wine grape yield. The best prediction was made for 2020 at VER, with the model overestimating the yield per hectare by 8 %, with the average absolute error for the entire period being 17 %. The results show that with this approach, it is possible to estimate wine grape yield accurately in advance at different scales.This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.ElsevierRepositório da Universidade dos AçoresBarriguinha, AndréJardim, BrunoNeto, Miguel de CastroGil, Artur José Freire2022-12-05T10:20:29Z2022-102022-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.3/6479engBarriguinha, A., Jardim, B., Castro Neto, M. & Gil, A. (2022). Using NDVI, climate data and machine learning to estimate yield in the Douro wine region. "International Journal of Applied Earth Observation and Geoinformation", 114, 103069. DOI:10.1016/j.jag.2022.1030691569-843210.1016/j.jag.2022.1030691872-826Xinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-12-20T14:35:01Zoai:repositorio.uac.pt:10400.3/6479Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:28:36.372646Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Using NDVI, climate data and machine learning to estimate yield in the Douro wine region
title Using NDVI, climate data and machine learning to estimate yield in the Douro wine region
spellingShingle Using NDVI, climate data and machine learning to estimate yield in the Douro wine region
Barriguinha, André
Remote Sensing
Vineyard
Yield
Estimation
Prediction
Climate
Machine Learning
NDVI
title_short Using NDVI, climate data and machine learning to estimate yield in the Douro wine region
title_full Using NDVI, climate data and machine learning to estimate yield in the Douro wine region
title_fullStr Using NDVI, climate data and machine learning to estimate yield in the Douro wine region
title_full_unstemmed Using NDVI, climate data and machine learning to estimate yield in the Douro wine region
title_sort Using NDVI, climate data and machine learning to estimate yield in the Douro wine region
author Barriguinha, André
author_facet Barriguinha, André
Jardim, Bruno
Neto, Miguel de Castro
Gil, Artur José Freire
author_role author
author2 Jardim, Bruno
Neto, Miguel de Castro
Gil, Artur José Freire
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade dos Açores
dc.contributor.author.fl_str_mv Barriguinha, André
Jardim, Bruno
Neto, Miguel de Castro
Gil, Artur José Freire
dc.subject.por.fl_str_mv Remote Sensing
Vineyard
Yield
Estimation
Prediction
Climate
Machine Learning
NDVI
topic Remote Sensing
Vineyard
Yield
Estimation
Prediction
Climate
Machine Learning
NDVI
description The authors gratefully acknowledge: IVDP - Instituto dos Vinhos do Douro e do Porto, IP (Institute of Douro and Port Wines) (https://www.ivdp.pt/en), for providing historical data related to wine grape production for the entire DDR at the parish level; IPMA - Instituto Português do Mar e da Atmosfera, IP (Portuguese Institute for Sea and Atmosphere)
publishDate 2022
dc.date.none.fl_str_mv 2022-12-05T10:20:29Z
2022-10
2022-10-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.3/6479
url http://hdl.handle.net/10400.3/6479
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Barriguinha, A., Jardim, B., Castro Neto, M. & Gil, A. (2022). Using NDVI, climate data and machine learning to estimate yield in the Douro wine region. "International Journal of Applied Earth Observation and Geoinformation", 114, 103069. DOI:10.1016/j.jag.2022.103069
1569-8432
10.1016/j.jag.2022.103069
1872-826X
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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