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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
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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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 : |
LEADER 03406nam a2200289 a 4500
001 1061531
005 2022-09-05
008 2020 bl uuuu u0uu1 u #d
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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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Treinta y Tres. |
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Fecha actual : |
23/07/2021 |
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Actualizado : |
02/09/2022 |
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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 : |
DEVINCENZI, T.; JAURENA, M.; DURANTE, M.; SAVIAN, J.V.; CIAPPESONI, G.; NAVAJAS, E.; CIGANDA, V.; LATTANZI, F.; PARUELO, J. |
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Afiliación : |
THAIS DEVINCENZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN ALEJANDRO JAURENA BARRIOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTÍN DURANTE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Instituto Nacional de Tecnología Agropecuaria, Estación Experimental Agropecuaria INTA Concepción Del Uruguay, Concepción del Uruguay, Argentina; JEAN VICTOR SAVIAN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS GABRIEL CIAPPESONI SCARONE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELLY ANA NAVAJAS VALENTINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VERONICA SOLANGE CIGANDA BRASCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO A. LATTANZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ PARUELO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Building the GLENCOE Platform -Grasslands LENding eConomic and ecOsystems sErvices. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Frontiers in Sustainable Food Systems, June 2021, volume 516, Article 547301. OPEN ACCESS. Doi: https://doi.org/10.3389/fsufs.2021.547301 |
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ISSN : |
2571-581X |
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DOI : |
10.3389/fsufs.2021.547301 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 31 March 2020, Accepted 26 April 2021, Published 16 June 2021.
Edited by: Iain James Gordon, Australian National University, Australia. Reviewed by: Karl Behrendt, Harper Adams University, United Kingdom; Kamaljit Kaur Sangha, Charles Darwin University, Australia.
This article is part of the research topic: Grazing in Future Multi-scapes: From Thought scapes to Landscapes, Creating Health from the Ground Up (Url: https://www.frontiersin.org/research-topics/11179/grazing-in-future-multi-scapes-from-thoughtscapes-to-landscapes-creating-health-from-the-ground-up#articles)
Correspondence: Devincenzi, T.; Instituto Nacional de Investigación Agropecuaria (INIA), Programa Producción de Carne y Lana, Estación Experimental INIA Tacuarembó, Tacuarembó, Uruguay; email: tdevincenzi@inia.org.uy |
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Contenido : |
ABSTRACT - To feed the rising population whilst also preserving ecosystem functions, creative solutions are needed for the ecological intensification of natural grassland-based livestock systems. In Uruguay, natural grasslands are the main nutritional resource for livestock production. In these ecosystems, cattle and sheep graze together all the year round, and grasslands are frequently heavily grazed. Considerable research has been generated concerning grassland management, but there is still no knowledge about the impact of decision rules that supports management actions on long-term ecosystem functioning, at the system level. To meet this deficit, a participatory working group of farmers, researchers, and consultants have developed the GLENCOE platform. This platform is a large-scale facility, supported by INIA-Uruguay, designed to answer the following question: How to intensify the grazing management to improve the sustainability of livestock systems based on natural grasslands? To build the platform three steps were followed: (I) definition of the research problem using a problem tree analysis; (ii) conceptualization of the platform and the design of the grazing systems to be evaluated; and, (iii) spatial allocation of the grazing systems according to the variability of soil, slopes, and seasonal dynamic of vegetation indexes. These criteria were considered across farmlets that were equivalent in the initial stage, allowing causal inferences for the systems trajectories on productive and environmental traits. The platform is composed of three independent farmlets of 50 ha each, where multiparous Hereford cows and Merinos wethers co-graze under three grazing management systems. Each farmlet is managed according to different spatio-temporal decisions of the specific management of vegetation communities, grazing methods, and the stockpile of forage that is allowed by the number of the existing paddocks. Farmlet-1; comprises less decisions (2 paddocks), Farmlet-2; intermediate (8 paddocks), and Farmlet-3; high level of decisions (32 paddocks). This innovative platform will be used as a participatory and interdisciplinary space for research and co-learning of management on processes that can only be observed in long-term evaluations, and at farmlet scale. We expect that this new approach will contribute to the developement and implemention of sustainable grazing management systems in Uruguay.
© Copyright © 2021 Devincenzi, Jaurena, Durante, Savian, Ciappesoni, Navajas, Ciganda, Lattanzi and Paruelo. MenosABSTRACT - To feed the rising population whilst also preserving ecosystem functions, creative solutions are needed for the ecological intensification of natural grassland-based livestock systems. In Uruguay, natural grasslands are the main nutritional resource for livestock production. In these ecosystems, cattle and sheep graze together all the year round, and grasslands are frequently heavily grazed. Considerable research has been generated concerning grassland management, but there is still no knowledge about the impact of decision rules that supports management actions on long-term ecosystem functioning, at the system level. To meet this deficit, a participatory working group of farmers, researchers, and consultants have developed the GLENCOE platform. This platform is a large-scale facility, supported by INIA-Uruguay, designed to answer the following question: How to intensify the grazing management to improve the sustainability of livestock systems based on natural grasslands? To build the platform three steps were followed: (I) definition of the research problem using a problem tree analysis; (ii) conceptualization of the platform and the design of the grazing systems to be evaluated; and, (iii) spatial allocation of the grazing systems according to the variability of soil, slopes, and seasonal dynamic of vegetation indexes. These criteria were considered across farmlets that were equivalent in the initial stage, allowing causal inferences for the systems trajectories... Presentar Todo |
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Palabras claves : |
Beef-cattle; Campos grasslands; Mixed-grazing; Rangelands; Sustainable intensification. |
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Asunto categoría : |
L02 Alimentación animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16658/1/fsufs-05-547301.pdf
https://www.frontiersin.org/articles/10.3389/fsufs.2021.547301/pdf
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Marc : |
LEADER 04354naa a2200313 a 4500
001 1062321
005 2022-09-02
008 2021 bl uuuu u00u1 u #d
022 $a2571-581X
024 7 $a10.3389/fsufs.2021.547301$2DOI
100 1 $aDEVINCENZI, T.
245 $aBuilding the GLENCOE Platform -Grasslands LENding eConomic and ecOsystems sErvices.$h[electronic resource]
260 $c2021
500 $aArticle history: Received 31 March 2020, Accepted 26 April 2021, Published 16 June 2021. Edited by: Iain James Gordon, Australian National University, Australia. Reviewed by: Karl Behrendt, Harper Adams University, United Kingdom; Kamaljit Kaur Sangha, Charles Darwin University, Australia. This article is part of the research topic: Grazing in Future Multi-scapes: From Thought scapes to Landscapes, Creating Health from the Ground Up (Url: https://www.frontiersin.org/research-topics/11179/grazing-in-future-multi-scapes-from-thoughtscapes-to-landscapes-creating-health-from-the-ground-up#articles) Correspondence: Devincenzi, T.; Instituto Nacional de Investigación Agropecuaria (INIA), Programa Producción de Carne y Lana, Estación Experimental INIA Tacuarembó, Tacuarembó, Uruguay; email: tdevincenzi@inia.org.uy
520 $aABSTRACT - To feed the rising population whilst also preserving ecosystem functions, creative solutions are needed for the ecological intensification of natural grassland-based livestock systems. In Uruguay, natural grasslands are the main nutritional resource for livestock production. In these ecosystems, cattle and sheep graze together all the year round, and grasslands are frequently heavily grazed. Considerable research has been generated concerning grassland management, but there is still no knowledge about the impact of decision rules that supports management actions on long-term ecosystem functioning, at the system level. To meet this deficit, a participatory working group of farmers, researchers, and consultants have developed the GLENCOE platform. This platform is a large-scale facility, supported by INIA-Uruguay, designed to answer the following question: How to intensify the grazing management to improve the sustainability of livestock systems based on natural grasslands? To build the platform three steps were followed: (I) definition of the research problem using a problem tree analysis; (ii) conceptualization of the platform and the design of the grazing systems to be evaluated; and, (iii) spatial allocation of the grazing systems according to the variability of soil, slopes, and seasonal dynamic of vegetation indexes. These criteria were considered across farmlets that were equivalent in the initial stage, allowing causal inferences for the systems trajectories on productive and environmental traits. The platform is composed of three independent farmlets of 50 ha each, where multiparous Hereford cows and Merinos wethers co-graze under three grazing management systems. Each farmlet is managed according to different spatio-temporal decisions of the specific management of vegetation communities, grazing methods, and the stockpile of forage that is allowed by the number of the existing paddocks. Farmlet-1; comprises less decisions (2 paddocks), Farmlet-2; intermediate (8 paddocks), and Farmlet-3; high level of decisions (32 paddocks). This innovative platform will be used as a participatory and interdisciplinary space for research and co-learning of management on processes that can only be observed in long-term evaluations, and at farmlet scale. We expect that this new approach will contribute to the developement and implemention of sustainable grazing management systems in Uruguay. © Copyright © 2021 Devincenzi, Jaurena, Durante, Savian, Ciappesoni, Navajas, Ciganda, Lattanzi and Paruelo.
653 $aBeef-cattle
653 $aCampos grasslands
653 $aMixed-grazing
653 $aRangelands
653 $aSustainable intensification
700 1 $aJAURENA, M.
700 1 $aDURANTE, M.
700 1 $aSAVIAN, J.V.
700 1 $aCIAPPESONI, G.
700 1 $aNAVAJAS, E.
700 1 $aCIGANDA, V.
700 1 $aLATTANZI, F.
700 1 $aPARUELO, J.
773 $tFrontiers in Sustainable Food Systems, June 2021, volume 516, Article 547301. OPEN ACCESS. Doi: https://doi.org/10.3389/fsufs.2021.547301
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