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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
001 1061456
005 2022-09-05
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 Las Brujas. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
26/06/2018 |
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Tipo de producción científica : |
Documentos |
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Autor : |
CONDE, P.; VILLAMIL, J.J.; FREDES, A.; BRUZZONE, J.; MARTINEZ, C.; MONTELONGO, M.J.; ELLIS, A.C.; GÁMBARO, A.; LEONI, C. |
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Afiliación : |
ANA PAULA CONDE INNAMORATO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN JOSE VILLAMIL SILVA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALEJANDRO DAVID FREDES SIVOPLAS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JULIANA BRUZZONE PIZZORNO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANA CECILIA MARTINEZ ESTEFAN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARÍA JOSÉ MONTELONGO; ANA CLAUDIA ELLIS; ADRIANA GÁMBARO; CAROLINA LEONI VELAZCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Determinación del umbral máximo tolerable de fruta infectada por colletotrichum Spp. para la obtención de aceite de oliva extra virgen. |
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Fecha de publicación : |
2013 |
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Fuente / Imprenta : |
In: INIA Las Brujas; Programa Nacional Producción Frutícola. Resultados experimentales en olivos. Canelones (UY): INIA, 2013. |
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Páginas : |
p. 31-37 |
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Serie : |
(INIA Serie Actividades de Difusión; 721) |
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ISSN : |
1688-9258 |
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Idioma : |
Español |
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Thesagro : |
ACEITE DE OLIVA; CONSUMO; CONTROL DE PLAGAS; OLEAGINOSAS; OLIVICULTURA; OLIVO. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/10499/1/sad-721-p.-31-37.pdf
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Marc : |
LEADER 00979naa a2200313 a 4500
001 1012548
005 2018-06-26
008 2013 bl uuuu u00u1 u #d
022 $a1688-9258
100 1 $aCONDE, P.
245 $aDeterminación del umbral máximo tolerable de fruta infectada por colletotrichum Spp. para la obtención de aceite de oliva extra virgen.
260 $c2013
300 $ap. 31-37
490 $a(INIA Serie Actividades de Difusión; 721)
650 $aACEITE DE OLIVA
650 $aCONSUMO
650 $aCONTROL DE PLAGAS
650 $aOLEAGINOSAS
650 $aOLIVICULTURA
650 $aOLIVO
700 1 $aVILLAMIL, J.J.
700 1 $aFREDES, A.
700 1 $aBRUZZONE, J.
700 1 $aMARTINEZ, C.
700 1 $aMONTELONGO, M.J.
700 1 $aELLIS, A.C.
700 1 $aGÁMBARO, A.
700 1 $aLEONI, C.
773 $tIn: INIA Las Brujas; Programa Nacional Producción Frutícola. Resultados experimentales en olivos. Canelones (UY): INIA, 2013.
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INIA Las Brujas (LB) |
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