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
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/145107
Resumo: Barriguinha, A., Jardim, B., De 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(November), 1-14. [103069]. https://doi.org/10.1016/j.jag.2022.103069 -- Funding: 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 Portuguˆes do Mar e da Atmosfera, IP (Portuguese Institute for Sea and Atmosphere)
id RCAP_7f55370d4fa597d18d3a145f9f4b9a21
oai_identifier_str oai:run.unl.pt:10362/145107
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Using NDVI, climate data and machine learning to estimate yield in the Douro wine regionRemote sensingVineyardYieldPredictionNDVIClimatemachine learningGlobal and Planetary ChangeEarth-Surface ProcessesComputers in Earth SciencesManagement, Monitoring, Policy and LawSDG 13 - Climate ActionBarriguinha, A., Jardim, B., De 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(November), 1-14. [103069]. https://doi.org/10.1016/j.jag.2022.103069 -- Funding: 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 Portuguˆes 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.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBarriguinha, AndréJardim, BrunoNeto, Miguel de CastroGil, Artur2022-10-28T22:11:05Z2022-11-012022-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/145107eng1569-8432PURE: 47359819https://doi.org/10.1016/j.jag.2022.103069info: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:RCAAP2024-03-11T05:25:15Zoai:run.unl.pt:10362/145107Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:55.203469Repositório 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
Prediction
NDVI
Climate
machine learning
Global and Planetary Change
Earth-Surface Processes
Computers in Earth Sciences
Management, Monitoring, Policy and Law
SDG 13 - Climate Action
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
author_role author
author2 Jardim, Bruno
Neto, Miguel de Castro
Gil, Artur
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Barriguinha, André
Jardim, Bruno
Neto, Miguel de Castro
Gil, Artur
dc.subject.por.fl_str_mv Remote sensing
Vineyard
Yield
Prediction
NDVI
Climate
machine learning
Global and Planetary Change
Earth-Surface Processes
Computers in Earth Sciences
Management, Monitoring, Policy and Law
SDG 13 - Climate Action
topic Remote sensing
Vineyard
Yield
Prediction
NDVI
Climate
machine learning
Global and Planetary Change
Earth-Surface Processes
Computers in Earth Sciences
Management, Monitoring, Policy and Law
SDG 13 - Climate Action
description Barriguinha, A., Jardim, B., De 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(November), 1-14. [103069]. https://doi.org/10.1016/j.jag.2022.103069 -- Funding: 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 Portuguˆes do Mar e da Atmosfera, IP (Portuguese Institute for Sea and Atmosphere)
publishDate 2022
dc.date.none.fl_str_mv 2022-10-28T22:11:05Z
2022-11-01
2022-11-01T00:00:00Z
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/10362/145107
url http://hdl.handle.net/10362/145107
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1569-8432
PURE: 47359819
https://doi.org/10.1016/j.jag.2022.103069
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
application/pdf
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
_version_ 1799138111638208512

Registros relacionados