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| 1. |  | SOUZA CONGIO, G. F. DE; BANNINK, A.; MAYORGA, MOGOLLÓN, O. L.; NICOLOV HRISTOV, A.; JAURENA, G.; GONDA, H.; GERE, J. I.; CERÓN-CUCCHI, M.E.; ORTIZ-CHURA, A.; TIERI, M.P.; HERNÁNDEZ, O.; RICCI, P.; JULIARENA, M.P.; LOMBARDI, B.; ABDALLA, A.L.; ABDALLA-FILHO, A.L.; BERNDT, A.; ANCHAO OLIVEIRA, P. P.; HENRIQUE, F. L.; GOMEZ MONTEIRO, A.L.; BORGES, L. I.; RIBEIRO-FILHO, H.M.N.; RIBEIRO PEREIRA, L.G.; RIBEIRO TOMICH, T.; MAGALHAES CAMPOS, M.; SAMARINI MACHADO, F.; MARCONDES, M. I.; ZERLOTTI MERCADANTE, M. E.; SANNOMIYA SAKAMOTO, L.; GALVAO ALBUQUERQUE, L.; FACCIO CARVALHO, P. C. DE; ROSSETTO, J.; SAVIAN, J.V.; MAZZA RODRIGUES, P. H.; PERNA JÚNIOR, F.; MOREIRA, T.S.; MAURÍCIO, R. M.; PACHECO RODRIGUES, J.P.; CRUZ BORGES, A.L. DA C.; REIS E SILVA, R.; FERREIRA LAGE, H.; ANDRADE REIS, R.; RUGGIERI, A.C.; CARDOSO, A. DA SILVA; SILVA, S. CARNEIRO DA; BARBOSA CHIAVEGATO, M.; VALADARES-FILHO, S. DE CAMPOS; SILVA, F. A. DE SALES; ZANETTI, D.; BERCHIELLI, T.T.; DUARTE MESSANA, J.; MUÑOZ, C.; ARIZA-NIETO, C.J.; SIERRA-ALARCÓN, L.I.; GUALDRÓN-DUARTE, L.B.; MESTRA-VARGAS, L.I.; MOLINA-BOTERO, I. C.; BARAHONA-ROSALES, R.; ARANGO, J.; GAVIRIA-URIBE, X.; GIRALDO VALDERRAMA, L.A.; ROSERO-NOGUERA, J.R.; POSADA-OCHOA, S.L.; ABARCA-MONGE, SERGIO; SOTO-BLANCO, R.; KU-VERA, J.C.; JIMÉNEZ-OCAMPO, R.; FLORES-SANTIAGO, E. DEL J.; CASTELÁN-ORTEGA, O.A.; VÁZQUEZ-CARRILLO, M.F.; BENAOUDA, M.; GÓMEZ-BRAVO, C. A.; ALVARADO BOLOVICH, V.I.; DÍAZ CÉSPEDES, M.A.; ASTIGARRAGA, L. Enteric methane mitigation strategies for ruminant livestock systems in the Latin America and Caribbean region: a meta-analysis. Journal of Cleaner Production. 2021. v. 312, art. 127693, 13 p. Doi: https://doi.org/10.1016/j.jclepro.2021.127693 Article history: Receiived 22 December 2020; Received in revised form 24 April 2021; Accepted 25 May 2021. Available online 30 May 2021.
Latin America Methane Project Collaborators está integrada por 71 investigadores en este artículo.| Biblioteca(s): INIA Treinta y Tres. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
29/10/2020 |
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Actualizado : |
21/03/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
AHMAD, R.; YANG, B.; ETTLIN, G.; BERGER, A.; RODRÍGUEZ-BOCCA, P. |
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Afiliación : |
REHAAN AHMAD, Cupertino High School, 10100 Finch Avenue, Cupertino, CA 95014, USA.; Cupertino High School, 10100 Finch Avenue, Cupertino, CA 95014, USA.; GUILLERMO ETTLIN, Facultad de Ingeniería, Instituto de Computación, Universidad de la República, Julio Herrera y Reissig 565, Montevideo 11300, Uruguay.; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO RODRÍGUEZ BOCCA, Facultad de Ingeniería, Instituto de Computación, Universidad de la República, Julio Herrera y Reissig 565, Montevideo 11300, Uruguay. |
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Título : |
A machine-learning based ConvLSTM architecture for NDVI forecasting. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
International Transactions in Operational Research, 2023, Volume 30, Issue 4, Pages 2025 - 2048. doi: https://doi.org/10.1111/itor.12887 |
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ISSN : |
0969-6016 (print); 1475-3995 (electronic) |
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DOI : |
10.1111/itor.12887 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 24 September 2019; Received in revised form 7 August 2020; Accepted 5 October 2020: First published 22 October 2020. -- Corresponding author: Rodríguez-Bocca, P.; Facultad de Ingeniería, Instituto de Computación, Universidad de la República, Julio Herrera y Reissig 565, Montevideo, Uruguay; email:prbocca@fing.edu.uy -- FUNDING: This research was partially supported by the "Comisión Sectorial de Investigación Científica (CSIC), UDELAR" and the "Programa de Desarrollo de las Ciencias Básicas (PEDECIBA)" of Uruguay. Some of the calculations reported in this paper were performed in ClusterUY, a newly installed platform for high-performance scientific computing at the National Supercomputing Center, Uruguay. -- Special Issue: OR and Big Data in Agriculture. |
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Contenido : |
Abstract:Normalized difference vegetation index (NDVI) is an essential remote measurement for agricultural studies because of its strong correlation with crop growth and yield. Accurate and comprehensive NDVI forecasts thus provide effective future projections of crop yield for precise agricultural planning and budgeting. Previous recurrent neural network (RNN) based forecasting methodologies have only performed single-pixel or large-area-average NDVI predictions. We present an alternative RNN-based deep-learning architecture, the convolutional long short-term memory (ConvLSTM), to supply much more comprehensive and detailed NDVI forecasts. In this paper, a single ConvLSTM is capable of 10,000-pixel field-level NDVI predictions, providing a more practical methodology for agricultural producers than single-pixel studies. We compare our model to the parametric crop growth model (PCGM), another multipixel field-level NDVI forecasting technique. We test each model over the same set of soybean crop field pixels with the root mean square error (RMSE) metric. The training configuration of each model is defined by the number of seasons of historical data used for weight optimization. When the best training configuration of the model found is used, the ConvLSTM obtains an RMSE of 0.0782, outperforming the PCGM?s RMSE of 0.0989 (an improvement of 0.0207 in precision represents a large gain in the accuracy of production volume prediction when projected into large production areas). Finally, by comparing the ConvLSTM predictions with the ground truth data over the entire target region rather than just the soybean crop pixels, we discover that the ConvLSTM can also predict NDVI values over the nonsoybean crop as effectively. © 2020 The Authors. MenosAbstract:Normalized difference vegetation index (NDVI) is an essential remote measurement for agricultural studies because of its strong correlation with crop growth and yield. Accurate and comprehensive NDVI forecasts thus provide effective future projections of crop yield for precise agricultural planning and budgeting. Previous recurrent neural network (RNN) based forecasting methodologies have only performed single-pixel or large-area-average NDVI predictions. We present an alternative RNN-based deep-learning architecture, the convolutional long short-term memory (ConvLSTM), to supply much more comprehensive and detailed NDVI forecasts. In this paper, a single ConvLSTM is capable of 10,000-pixel field-level NDVI predictions, providing a more practical methodology for agricultural producers than single-pixel studies. We compare our model to the parametric crop growth model (PCGM), another multipixel field-level NDVI forecasting technique. We test each model over the same set of soybean crop field pixels with the root mean square error (RMSE) metric. The training configuration of each model is defined by the number of seasons of historical data used for weight optimization. When the best training configuration of the model found is used, the ConvLSTM obtains an RMSE of 0.0782, outperforming the PCGM?s RMSE of 0.0989 (an improvement of 0.0207 in precision represents a large gain in the accuracy of production volume prediction when projected into large production areas). Fin... Presentar Todo |
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Palabras claves : |
CONVLSTM NEURAL NETWORKS; DEEP LEARNING; NORMALIZED DIFFERENCE VEGETATION INDEX; OPTIMIZATION; PREDICTIVE ANALYSIS. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
https://onlinelibrary.wiley.com/doi/epdf/10.1111/itor.12887
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Marc : |
LEADER 03430naa a2200265 a 4500
001 1061440
005 2023-03-21
008 2023 bl uuuu u00u1 u #d
022 $a0969-6016 (print); 1475-3995 (electronic)
024 7 $a10.1111/itor.12887$2DOI
100 1 $aAHMAD, R.
245 $aA machine-learning based ConvLSTM architecture for NDVI forecasting.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 24 September 2019; Received in revised form 7 August 2020; Accepted 5 October 2020: First published 22 October 2020. -- Corresponding author: Rodríguez-Bocca, P.; Facultad de Ingeniería, Instituto de Computación, Universidad de la República, Julio Herrera y Reissig 565, Montevideo, Uruguay; email:prbocca@fing.edu.uy -- FUNDING: This research was partially supported by the "Comisión Sectorial de Investigación Científica (CSIC), UDELAR" and the "Programa de Desarrollo de las Ciencias Básicas (PEDECIBA)" of Uruguay. Some of the calculations reported in this paper were performed in ClusterUY, a newly installed platform for high-performance scientific computing at the National Supercomputing Center, Uruguay. -- Special Issue: OR and Big Data in Agriculture.
520 $aAbstract:Normalized difference vegetation index (NDVI) is an essential remote measurement for agricultural studies because of its strong correlation with crop growth and yield. Accurate and comprehensive NDVI forecasts thus provide effective future projections of crop yield for precise agricultural planning and budgeting. Previous recurrent neural network (RNN) based forecasting methodologies have only performed single-pixel or large-area-average NDVI predictions. We present an alternative RNN-based deep-learning architecture, the convolutional long short-term memory (ConvLSTM), to supply much more comprehensive and detailed NDVI forecasts. In this paper, a single ConvLSTM is capable of 10,000-pixel field-level NDVI predictions, providing a more practical methodology for agricultural producers than single-pixel studies. We compare our model to the parametric crop growth model (PCGM), another multipixel field-level NDVI forecasting technique. We test each model over the same set of soybean crop field pixels with the root mean square error (RMSE) metric. The training configuration of each model is defined by the number of seasons of historical data used for weight optimization. When the best training configuration of the model found is used, the ConvLSTM obtains an RMSE of 0.0782, outperforming the PCGM?s RMSE of 0.0989 (an improvement of 0.0207 in precision represents a large gain in the accuracy of production volume prediction when projected into large production areas). Finally, by comparing the ConvLSTM predictions with the ground truth data over the entire target region rather than just the soybean crop pixels, we discover that the ConvLSTM can also predict NDVI values over the nonsoybean crop as effectively. © 2020 The Authors.
653 $aCONVLSTM NEURAL NETWORKS
653 $aDEEP LEARNING
653 $aNORMALIZED DIFFERENCE VEGETATION INDEX
653 $aOPTIMIZATION
653 $aPREDICTIVE ANALYSIS
700 1 $aYANG, B.
700 1 $aETTLIN, G.
700 1 $aBERGER, A.
700 1 $aRODRÍGUEZ-BOCCA, P.
773 $tInternational Transactions in Operational Research, 2023, Volume 30, Issue 4, Pages 2025 - 2048. doi: https://doi.org/10.1111/itor.12887
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