Fecha/hora: 23/05/2024 03:10:54

Biblioteca(s): 	INIA La Estanzuela.	
Fecha corriente: 	30/11/2020	
Fecha de la última actualización: 	05/09/2022	
Autoria: 	WANG, X.; SILVA, P.; BELLO, N.M.; SINGH, D.; EVERS, B.; SINGH, R.P.; POLAND, J.	
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.	
Título: 	Improved accuracy of high-throughput phenotyping from unmanned aerial systems by extracting traits directly from orthorectified images.	
Año de publicación: 	2020	
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	
DOI: 	10.3389/fpls.2020.587093	
Idioma: 	Inglés	
Notas: 	Article history: Received: 27 July 2020/ Accepted: 30 September 2020/Published: 21 October 2020.	
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.	
Palabras-Clave: 	CANOPY TEMPERATURE; GROUND COVER; HIGH-THROUGHPUT PHENOTYPING; NORMALIZED DIFFERENCE VEGETATION INDEX; UNMANNED AERIAL SYSTEMS; WHEAT.	
Thesagro: 	TRIGO.	
Categoría del tema: 	--	
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	
Registro original: 	INIA La Estanzuela (LE)	
Ejemplares: 	LE - (103251-1) - PXI - DD - DD - PP/Frontiers in Plant Science/2020 -  -  - DIS