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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
02/04/2020 |
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Actualizado : |
24/02/2022 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
HELGUERA, M.; ABUGALIEVA, A.; BATTENFIELD, S.; BÉKÉS, F.; BRANLARD, G.; CUNIBERTI, M.; HÜSKEN,A.; JOHANSSON, E.; MORRIS, C.F.; NURIT, E.; SISSONS, M.; VÁZQUEZ, D. |
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Afiliación : |
MARCELO HELGUERA, National Institute of Agricultural Technology (INTA), Marcos Juárez, Argentina .; AIGUL ABUGALIEVA, Kazakh Scientific Research Institute of Agriculture and Plant Growing, Almalybak, Kazakhstan.; SARAH BATTENFIELD, Syngenta, Junction City, KS, USA.; FERENC BÉKÉS, FBFD PTY LTD, Sydney, NSW, Australia.; GÉRARD BRANLARD, INRAE, UCA UMR1095 GDEC, Clermont-Ferrand, France.; MARTHA CUNIBERTI, Wheat and Soybean Quality Laboratory, National Institute of Agricultural Technology (INTA), Buenos Aires, Argentina.; ALEXANDRA HÜSKEN, Department of Safety and Quality of CerealsMax Rubner-Institut, Federal Research Institute of Nutrition and Food Detmold, Germany.; EVA JOHANSSON, Department of Plant Breeding The Swedish University of Agricultural Sciences, Alnarp, Sweden.; CRAIG F. MORRIS, Western Wheat Quality LaboratoryUSDA-ARS,Pullman,USA.; ERIC NURIT, Mazan,France.; MIKE SISSONS, NSW Department of Primary Industries Tamworth Centre for Crop Improvement Calala, Australia.; DANIEL VÁZQUEZ PEYRONEL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Grain Quality in Breeding. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
In: Igrejas G., Ikeda T., Guzmán C. (eds). Wheat Quality For Improving Processing And Human Health. Cham:Springer. Doi:
https://doi.org/10.1007/978-3-030-34163-3_12 |
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Páginas : |
p. 273-307. |
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Idioma : |
Inglés |
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Notas : |
Article history:First Online: 18 March 2020. |
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Contenido : |
Abstract:
Technological (processing performance and end-product) and nutritional quality of wheat is in principle determined by a number of compounds within the wheat grain, including proteins, polysaccharides, lipids, minerals, heavy metals, vitamins and phytochemicals, effecting these characters. The genotype and environment is of similar importance for the determination of the content and composition of these compounds. Furthermore, the interaction between genotypes and the cultivation environment may play a significant role. Many studies have evaluated whether the genotype or the environment plays the major role in determining the content of the mentioned compounds. An overall conclusion of these studies is that except for compounds encoded by single major genes, importance of certain factors mainly depend on how wide environments and how diverse cultivars are within these comparative studies. Comparing environments all over, e.g. across Latin America, ends up with a high significance of the environment while large studies including genotypes of wide genetic background result in a significant role for the genotype. In addition, for some technological properties and components, genotype has a higher effect (e.g. grain hardness and gluten proteins), while environment influences stronger on others (e.g. protein and mineral content).Content and concentration of proteins, but also to some extent of starch, some non-starch polysaccharides and lipids, are essential in determining the technological quality of a wheat flour. For nutritional quality of the flour, the majority of the compounds are together the important determinant. Thus an increased understanding of environmental effects is essential. As to how the environment is influencing the content of the compounds, there are some differences. The protein content and composition is strongly affected by environmental factors influencing nitrogen availability and cultivar development time. However, these two factors are impacted by a range of environmental (temperature, precipitation, humidity/sun hours, etc.) and agronomic (soil properties, crop management practices such as seeding density, nitrogen fertilizer application timing and amount, etc.) components. Thus, to understand the interplay between the various environmental and agronomic factors impacting the technological quality of a wheat flour, modeling is a useful tool. Several other compounds, including minerals and heavy metals, are to a higher extent determined by site specific variation, resulting in similar rankings of entries across locations, although the total content is varying among years. The bioactive compounds and vitamins are a part of the defense mechanisms of plants and thus there is a variation in these compounds depending on prevailing biotic and abiotic stresses (heat, drought, excess rainfall, nutrition, diseases and pests). Thus, even for nutritional quality of wheat, incorporating all compounds of relevance in the evaluation would benefit from modeling tools. MenosAbstract:
Technological (processing performance and end-product) and nutritional quality of wheat is in principle determined by a number of compounds within the wheat grain, including proteins, polysaccharides, lipids, minerals, heavy metals, vitamins and phytochemicals, effecting these characters. The genotype and environment is of similar importance for the determination of the content and composition of these compounds. Furthermore, the interaction between genotypes and the cultivation environment may play a significant role. Many studies have evaluated whether the genotype or the environment plays the major role in determining the content of the mentioned compounds. An overall conclusion of these studies is that except for compounds encoded by single major genes, importance of certain factors mainly depend on how wide environments and how diverse cultivars are within these comparative studies. Comparing environments all over, e.g. across Latin America, ends up with a high significance of the environment while large studies including genotypes of wide genetic background result in a significant role for the genotype. In addition, for some technological properties and components, genotype has a higher effect (e.g. grain hardness and gluten proteins), while environment influences stronger on others (e.g. protein and mineral content).Content and concentration of proteins, but also to some extent of starch, some non-starch polysaccharides and lipids, are essential in determini... Presentar Todo |
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Palabras claves : |
CASE-STUDIES; DURUM-WHEAT; NUTRITIONAL-QUALITY; PLATAFORMA AGROALIMENTOS; QUALITY-SELECTION; SOFT-WHEAT; WILD-RELATIVES. |
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Thesagro : |
TRIGO. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
LEADER 04132naa a2200373 a 4500
001 1060983
005 2022-02-24
008 2020 bl uuuu u00u1 u #d
100 1 $aHELGUERA, M.
245 $aGrain Quality in Breeding.$h[electronic resource]
260 $c2020
300 $ap. 273-307.
500 $aArticle history:First Online: 18 March 2020.
520 $aAbstract: Technological (processing performance and end-product) and nutritional quality of wheat is in principle determined by a number of compounds within the wheat grain, including proteins, polysaccharides, lipids, minerals, heavy metals, vitamins and phytochemicals, effecting these characters. The genotype and environment is of similar importance for the determination of the content and composition of these compounds. Furthermore, the interaction between genotypes and the cultivation environment may play a significant role. Many studies have evaluated whether the genotype or the environment plays the major role in determining the content of the mentioned compounds. An overall conclusion of these studies is that except for compounds encoded by single major genes, importance of certain factors mainly depend on how wide environments and how diverse cultivars are within these comparative studies. Comparing environments all over, e.g. across Latin America, ends up with a high significance of the environment while large studies including genotypes of wide genetic background result in a significant role for the genotype. In addition, for some technological properties and components, genotype has a higher effect (e.g. grain hardness and gluten proteins), while environment influences stronger on others (e.g. protein and mineral content).Content and concentration of proteins, but also to some extent of starch, some non-starch polysaccharides and lipids, are essential in determining the technological quality of a wheat flour. For nutritional quality of the flour, the majority of the compounds are together the important determinant. Thus an increased understanding of environmental effects is essential. As to how the environment is influencing the content of the compounds, there are some differences. The protein content and composition is strongly affected by environmental factors influencing nitrogen availability and cultivar development time. However, these two factors are impacted by a range of environmental (temperature, precipitation, humidity/sun hours, etc.) and agronomic (soil properties, crop management practices such as seeding density, nitrogen fertilizer application timing and amount, etc.) components. Thus, to understand the interplay between the various environmental and agronomic factors impacting the technological quality of a wheat flour, modeling is a useful tool. Several other compounds, including minerals and heavy metals, are to a higher extent determined by site specific variation, resulting in similar rankings of entries across locations, although the total content is varying among years. The bioactive compounds and vitamins are a part of the defense mechanisms of plants and thus there is a variation in these compounds depending on prevailing biotic and abiotic stresses (heat, drought, excess rainfall, nutrition, diseases and pests). Thus, even for nutritional quality of wheat, incorporating all compounds of relevance in the evaluation would benefit from modeling tools.
650 $aTRIGO
653 $aCASE-STUDIES
653 $aDURUM-WHEAT
653 $aNUTRITIONAL-QUALITY
653 $aPLATAFORMA AGROALIMENTOS
653 $aQUALITY-SELECTION
653 $aSOFT-WHEAT
653 $aWILD-RELATIVES
700 1 $aABUGALIEVA, A.
700 1 $aBATTENFIELD, S.
700 1 $aBÉKÉS, F.
700 1 $aBRANLARD, G.
700 1 $aCUNIBERTI, M.
700 1 $aHÜSKEN,A.
700 1 $aJOHANSSON, E.
700 1 $aMORRIS, C.F.
700 1 $aNURIT, E.
700 1 $aSISSONS, M.
700 1 $aVÁZQUEZ, D.
773 $tIn: Igrejas G., Ikeda T., Guzmán C. (eds). Wheat Quality For Improving Processing And Human Health. Cham:Springer. Doi: https://doi.org/10.1007/978-3-030-34163-3_12
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INIA La Estanzuela (LE) |
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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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