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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
05/11/2020 |
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Actualizado : |
05/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
TREVISAN, R.; PÉREZ, O.; SCHMITZ, N.; DIERS, B.; MARTIN, N |
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Afiliación : |
RODRIGO TREVISAN, Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA.; OSVALDO MARTIN PEREZ GONZALEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; NATHAN SCHMITZ, GDM Seeds Inc., Gibson City, IL 60936, USA.; BRIAN DIERS, Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA.; NICOLAS MARTIN, Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA. |
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Título : |
High-throughput phenotyping of soybean maturity using time Series UAV imagery and convolutional neural networks. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Remote Sensing, 2020, 12(21), 3617. OPEN ACCESS. DOI: https://doi.org/10.3390/rs12213617. |
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DOI : |
10.3390/rs12213617 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 18 September 2020 / Revised: 28 October 2020 / Accepted: 29 October 2020 / Published: 4 November 2020. |
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Contenido : |
Abstract: Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges.Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies
have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were
developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previous research and can be used at scale in commercial breeding programs. MenosAbstract: Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges.Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies
have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were
developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previ... Presentar Todo |
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Palabras claves : |
GLYCINE MAX (L.) MERR; MACHINE LEARNING; PHYSIOLOGICAL MATURITY; PLANT BREEDING; SOYBEAN PHENOLOGY. |
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Thesagro : |
MEJORAMIENTO GENETICO DE PLANTAS; SOJA. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14789/1/remotesensing-12-03617.pdf
https://www.mdpi.com/2072-4292/12/21/3617/htm#
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Marc : |
LEADER 02555naa a2200277 a 4500
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008 2020 bl uuuu u00u1 u #d
024 7 $a10.3390/rs12213617$2DOI
100 1 $aTREVISAN, R.
245 $aHigh-throughput phenotyping of soybean maturity using time Series UAV imagery and convolutional neural networks.$h[electronic resource]
260 $c2020
500 $aArticle history: Received: 18 September 2020 / Revised: 28 October 2020 / Accepted: 29 October 2020 / Published: 4 November 2020.
520 $aAbstract: Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges.Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previous research and can be used at scale in commercial breeding programs.
650 $aMEJORAMIENTO GENETICO DE PLANTAS
650 $aSOJA
653 $aGLYCINE MAX (L.) MERR
653 $aMACHINE LEARNING
653 $aPHYSIOLOGICAL MATURITY
653 $aPLANT BREEDING
653 $aSOYBEAN PHENOLOGY
700 1 $aPÉREZ, O.
700 1 $aSCHMITZ, N.
700 1 $aDIERS, B.
700 1 $aMARTIN, N
773 $tRemote Sensing, 2020, 12(21), 3617. OPEN ACCESS. DOI: https://doi.org/10.3390/rs12213617.
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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
30/05/2022 |
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Actualizado : |
30/05/2022 |
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Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
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Autor : |
AYALA, W.; BENDERSKY, D.; PARUELO, J.; ROVIRA, P.J. |
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Afiliación : |
WALTER FELIZARDO AYALA SILVERA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Instituto Nacional de Tecnología Agropecuaria (INTA, Argentina).; JOSÉ PARUELO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay.; PABLO JUAN ROVIRA SANZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
The importance of Campos ecosystems as a world food producer and as a provider of ecosystem services. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
In: MUREITHI, J.G.; NYANGITO, M.M.; WAMUONGO, J.W.; NJOKA, J.; NYAMBATI; E.M.; MIANO,D.; MBUKU, S.; OKOTI, M.; MARITIM. F., ed. Sustainable use of grassland and rangeland resources for improved livelihoods. Proceedings. Nairobi, (Kenia): KALRO, 2022. |
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Páginas : |
p. 655-660 |
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Idioma : |
Inglés |
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Palabras claves : |
HERBAGE PRODUCTION; NATIVE GRASSLANDS; SUSTAINABLE USE; VARIABILITY. |
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Asunto categoría : |
A50 Investigación agraria |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16450/1/Paper-124-The-importance-of-Campos-ecos-Ayala.pdf
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Marc : |
LEADER 00798nam a2200193 a 4500
001 1063181
005 2022-05-30
008 2022 bl uuuu u01u1 u #d
100 1 $aAYALA, W.
245 $aThe importance of Campos ecosystems as a world food producer and as a provider of ecosystem services.$h[electronic resource]
260 $aIn: MUREITHI, J.G.; NYANGITO, M.M.; WAMUONGO, J.W.; NJOKA, J.; NYAMBATI; E.M.; MIANO,D.; MBUKU, S.; OKOTI, M.; MARITIM. F., ed. Sustainable use of grassland and rangeland resources for improved livelihoods. Proceedings. Nairobi, (Kenia): KALRO$c2022
300 $ap. 655-660
653 $aHERBAGE PRODUCTION
653 $aNATIVE GRASSLANDS
653 $aSUSTAINABLE USE
653 $aVARIABILITY
700 1 $aBENDERSKY, D.
700 1 $aPARUELO, J.
700 1 $aROVIRA, P.J.
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INIA Treinta y Tres (TT) |
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