Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.5/30278 |
Resumo: | As agriculture has an increasing impact on the environment, new techniques can help meet future food needs while maintaining or reducing the environmental footprint. Those techniques must incorporate a range of sensing, communication, and data analysis technologies to make informed management decisions, such as those related to the use of water, fertilizer, pesticides, seeds, fuel, labor, etc., to help increase crop production and reduce water and nutrient losses, as well as negative environmental impacts. In this study, a Bidirectional Long Short-Term Memory (BiLSTM) model was trained on real data from Internet of Things sensors in a vineyard located in the Douro wine-growing region, from 2018–2021, to evaluate the ability of this model to predict the Fraction of Transpirable Soil Water (FTSW). The model uses historical data, including reference evapotranspiration, relative humidity, vapor pressure deficit, and rainfall, and outputs the FTSW for periods of one, three, five, and seven days. The model achieved an RMSE between 8.3% and 16.6% and an R2-score between 0.75 and 0.93. The model was validated on an independent dataset collected in 2002–2004 from a different vineyard located in the Lisbon wine-growing region, Portugal, and achieved an R2-score of 87% and an RMSE of 10.36%. Finally, the performance of the FTSW in the vineyard prediction model was compared with that of the Random Forest model, support vector regression, and linear regression. The results showed that BiLSTM performed better than the RF model on the unseen data, and the BiLSTM model can be considered a suitable model for the accurate prediction of the FTSW. |
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Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyardsagricultureFTSWdeep learningLSTMBiLSTMsupport decision-making algorithmsAs agriculture has an increasing impact on the environment, new techniques can help meet future food needs while maintaining or reducing the environmental footprint. Those techniques must incorporate a range of sensing, communication, and data analysis technologies to make informed management decisions, such as those related to the use of water, fertilizer, pesticides, seeds, fuel, labor, etc., to help increase crop production and reduce water and nutrient losses, as well as negative environmental impacts. In this study, a Bidirectional Long Short-Term Memory (BiLSTM) model was trained on real data from Internet of Things sensors in a vineyard located in the Douro wine-growing region, from 2018–2021, to evaluate the ability of this model to predict the Fraction of Transpirable Soil Water (FTSW). The model uses historical data, including reference evapotranspiration, relative humidity, vapor pressure deficit, and rainfall, and outputs the FTSW for periods of one, three, five, and seven days. The model achieved an RMSE between 8.3% and 16.6% and an R2-score between 0.75 and 0.93. The model was validated on an independent dataset collected in 2002–2004 from a different vineyard located in the Lisbon wine-growing region, Portugal, and achieved an R2-score of 87% and an RMSE of 10.36%. Finally, the performance of the FTSW in the vineyard prediction model was compared with that of the Random Forest model, support vector regression, and linear regression. The results showed that BiLSTM performed better than the RF model on the unseen data, and the BiLSTM model can be considered a suitable model for the accurate prediction of the FTSW.MDPIRepositório da Universidade de LisboaAlibabaei, KhadijehGaspar, Pedro D.Campos, Rebeca M.Rodrigues, Gonçalo C.Lopes, Carlos M.2024-03-05T16:52:05Z2023-032023-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/30278engAlibabaei, K.; Gaspar, P.D.; Campos, R.M.; Rodrigues, G.C.; Lopes, C.M. Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards. Appl. Sci. 2023, 13, 2815.10.3390/app13052815info: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:RCAAP2024-03-10T01:34:39Zoai:www.repository.utl.pt:10400.5/30278Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:14:15.784346Repositório 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 |
Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards |
title |
Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards |
spellingShingle |
Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards Alibabaei, Khadijeh agriculture FTSW deep learning LSTM BiLSTM support decision-making algorithms |
title_short |
Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards |
title_full |
Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards |
title_fullStr |
Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards |
title_full_unstemmed |
Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards |
title_sort |
Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards |
author |
Alibabaei, Khadijeh |
author_facet |
Alibabaei, Khadijeh Gaspar, Pedro D. Campos, Rebeca M. Rodrigues, Gonçalo C. Lopes, Carlos M. |
author_role |
author |
author2 |
Gaspar, Pedro D. Campos, Rebeca M. Rodrigues, Gonçalo C. Lopes, Carlos M. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Alibabaei, Khadijeh Gaspar, Pedro D. Campos, Rebeca M. Rodrigues, Gonçalo C. Lopes, Carlos M. |
dc.subject.por.fl_str_mv |
agriculture FTSW deep learning LSTM BiLSTM support decision-making algorithms |
topic |
agriculture FTSW deep learning LSTM BiLSTM support decision-making algorithms |
description |
As agriculture has an increasing impact on the environment, new techniques can help meet future food needs while maintaining or reducing the environmental footprint. Those techniques must incorporate a range of sensing, communication, and data analysis technologies to make informed management decisions, such as those related to the use of water, fertilizer, pesticides, seeds, fuel, labor, etc., to help increase crop production and reduce water and nutrient losses, as well as negative environmental impacts. In this study, a Bidirectional Long Short-Term Memory (BiLSTM) model was trained on real data from Internet of Things sensors in a vineyard located in the Douro wine-growing region, from 2018–2021, to evaluate the ability of this model to predict the Fraction of Transpirable Soil Water (FTSW). The model uses historical data, including reference evapotranspiration, relative humidity, vapor pressure deficit, and rainfall, and outputs the FTSW for periods of one, three, five, and seven days. The model achieved an RMSE between 8.3% and 16.6% and an R2-score between 0.75 and 0.93. The model was validated on an independent dataset collected in 2002–2004 from a different vineyard located in the Lisbon wine-growing region, Portugal, and achieved an R2-score of 87% and an RMSE of 10.36%. Finally, the performance of the FTSW in the vineyard prediction model was compared with that of the Random Forest model, support vector regression, and linear regression. The results showed that BiLSTM performed better than the RF model on the unseen data, and the BiLSTM model can be considered a suitable model for the accurate prediction of the FTSW. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-03 2023-03-01T00:00:00Z 2024-03-05T16:52:05Z |
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/10400.5/30278 |
url |
http://hdl.handle.net/10400.5/30278 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Alibabaei, K.; Gaspar, P.D.; Campos, R.M.; Rodrigues, G.C.; Lopes, C.M. Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards. Appl. Sci. 2023, 13, 2815. 10.3390/app13052815 |
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 |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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 |
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1799137795407609856 |