Fecha/hora: 12/06/2024 13:12:36

Biblioteca(s): 	INIA La Estanzuela.	
Fecha corriente: 	05/11/2020	
Fecha de la última actualización: 	05/09/2022	
Autoria: 	TREVISAN, R.; PÉREZ, O.; SCHMITZ, N.; DIERS, B.; MARTIN, N	
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.	
Título: 	High-throughput phenotyping of soybean maturity using time Series UAV imagery and convolutional neural networks.	
Año de publicación: 	2020	
Fuente/Imprenta: 	Remote Sensing, 2020, 12(21), 3617.  OPEN ACCESS. DOI: https://doi.org/10.3390/rs12213617.	
DOI: 	10.3390/rs12213617	
Idioma: 	Inglés	
Notas: 	Article history: Received: 18 September 2020 / Revised: 28 October 2020 / Accepted: 29 October 2020 / Published: 4 November 2020.	
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.	
Palabras-Clave: 	GLYCINE MAX (L.) MERR; MACHINE LEARNING; PHYSIOLOGICAL MATURITY; PLANT BREEDING; SOYBEAN PHENOLOGY.	
Thesagro: 	MEJORAMIENTO GENETICO DE PLANTAS; SOJA.	
Categoría del tema: 	F30 Genética vegetal y fitomejoramiento	
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#	
Registro original: 	INIA La Estanzuela (LE)	
Ejemplares: 	LE - (103236-1) - PXI - AP - DD - PP/Remote Sensing/2020 -  -  - DIS