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| 42. |  | WANG, X.; SILVA, P.; BELLO, N.M.; SINGH, D.; EVERS, B.; SINGH, R.P.; POLAND, J. Improved accuracy of high-throughput phenotyping from unmanned aerial systems by extracting traits directly from orthorectified images. Frontiers in Plant Science, 21 October 2020, Volume 11, Article number 587093. Open Access. Doi: https://doi.org/10.3389/fpls.2020.587093 Article history: Received: 27 July 2020/ Accepted: 30 September 2020/Published: 21 October 2020.| Biblioteca(s): INIA La Estanzuela. |
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| 44. | | BARAIBAR, S.; GARCIA, R.; SILVA, P.; LADO, B.; CASTRO, A.; GUTIÉRREZ , L.; KAVANOVÁ, M.; QUINCKE, M.; BHAVANI , S.; RANDHAWA, M.S.; GERMAN, S. QTL of resistance to Ug99 and other stem rust pathogen races in bread wheat. Molecular Breeding, 1 August 2020, Volume 40, Issue 8, Article number 82. DOI: https://doi.org/10.1007/s11032-020-01153-5 Article history: Received 26 June 2019/ Accepted 23 October 2019/ Published 15 August 2020.| Biblioteca(s): INIA La Estanzuela. |
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| 57. | | KOLMER, J.A.; ORDONEZ, M.E.; GERMAN, S.; MORGUNOV, A.; PRETORIUS,Z.A.; VISSER, B.; ANIKSTER, Y.; ACEVEDO, M. Multilocus genotypes of the wheat leaf rust fungus Puccinia triticina in worldwide regions indicate past and current long distance migration. Phytopathology, 2019,Apr 1: 10.1094/PHYTO-10-18-0411-R. [Epub ahead of print]. Article history: Published Online: 1 Apr 2019 .| Biblioteca(s): INIA La Estanzuela. |
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| Registros recuperados : 987 | |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
30/11/2020 |
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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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Circulación / Nivel : |
Internacional - -- |
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Autor : |
WANG, X.; SILVA, P.; BELLO, N.M.; SINGH, D.; EVERS, B.; SINGH, R.P.; POLAND, J. |
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Afiliación : |
XU WANG, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States.; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay.;Department of Plant Pathology, Kansas State University, Manhattan, KS, United States; Interdepartmental Genetics, Kansas State University, Manhattan, KS, United State; NORA M. BELLO, Department of Statistics, Kansas State University, Manhattan, KS, United States,; DALJIT SINGH, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States.; BYRON EVERS, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States.; RAVI P. SINGH, Global Wheat Program, International Maize and Wheat Improvement Center, Mexico City, Mexico.; JESSE POLAND1, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States. |
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Título : |
Improved accuracy of high-throughput phenotyping from unmanned aerial systems by extracting traits directly from orthorectified images. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Frontiers in Plant Science, 21 October 2020, Volume 11, Article number 587093. Open Access. Doi: https://doi.org/10.3389/fpls.2020.587093 |
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DOI : |
10.3389/fpls.2020.587093 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 27 July 2020/ Accepted: 30 September 2020/Published: 21 October 2020. |
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Contenido : |
The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based highthroughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial
images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo,probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broadsense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection. MenosThe development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based highthroughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial
images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo,probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broadsense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-s... Presentar Todo |
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Palabras claves : |
CANOPY TEMPERATURE; GROUND COVER; HIGH-THROUGHPUT PHENOTYPING; NORMALIZED DIFFERENCE VEGETATION INDEX; UNMANNED AERIAL SYSTEMS; WHEAT. |
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Thesagro : |
TRIGO. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16703/1/fpls-11-587093.pdf
https://www.frontiersin.org/articles/10.3389/fpls.2020.587093/pdf
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Marc : |
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024 7 $a10.3389/fpls.2020.587093$2DOI
100 1 $aWANG, X.
245 $aImproved accuracy of high-throughput phenotyping from unmanned aerial systems by extracting traits directly from orthorectified images.$h[electronic resource]
260 $aFrontiers in Plant Science, 21 October 2020, Volume 11, Article number 587093. Open Access. Doi: https://doi.org/10.3389/fpls.2020.587093$c2020
500 $aArticle history: Received: 27 July 2020/ Accepted: 30 September 2020/Published: 21 October 2020.
520 $aThe development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based highthroughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo,probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broadsense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.
650 $aTRIGO
653 $aCANOPY TEMPERATURE
653 $aGROUND COVER
653 $aHIGH-THROUGHPUT PHENOTYPING
653 $aNORMALIZED DIFFERENCE VEGETATION INDEX
653 $aUNMANNED AERIAL SYSTEMS
653 $aWHEAT
700 1 $aSILVA, P.
700 1 $aBELLO, N.M.
700 1 $aSINGH, D.
700 1 $aEVERS, B.
700 1 $aSINGH, R.P.
700 1 $aPOLAND, J.
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