Satellite imagery for high-throughput phenotyping in breeding plots

dc.creatorPinto Espinosa, F.
dc.creatorZaman-Allah, M.
dc.creatorReynolds, M.P.
dc.creatorSchulthess, U.
dc.date2023-06-01T20:20:12Z
dc.date2023-06-01T20:20:12Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:36Z
dc.date.available2023-07-17T20:10:36Z
dc.identifierhttps://hdl.handle.net/10883/22607
dc.identifier10.3389/fpls.2023.1114670
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514350
dc.descriptionAdvances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. Among these platforms, satellite imagery may represent one of the ultimate approaches to remotely monitor trials and nurseries planted in multiple locations while standardizing protocols and reducing costs. However, the deployment of satellite-based phenotyping in breeding trials has largely been limited by low spatial resolution of satellite images. The advent of a new generation of high-resolution satellites may finally overcome these limitations. The SkySat constellation started offering multispectral images at a 0.5 m resolution since 2020. In this communication we present a case study on the use of time series SkySat images to estimate NDVI from wheat and maize breeding plots encompassing different sizes and spacing. We evaluated the reliability of the calculated NDVI and tested its capacity to detect seasonal changes and genotypic differences. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs.
dc.languageEnglish
dc.publisherFrontiers
dc.relationhttps://figshare.com/collections/Satellite_imagery_for_high-throughput_phenotyping_in_breeding_plots/6648545
dc.relationClimate adaptation & mitigation
dc.relationEnvironmental health & biodiversity
dc.relationNutrition, health & food security
dc.relationAccelerated Breeding
dc.relationDigital Innovation
dc.relationResilient Agrifood Systems
dc.relationGenetic Innovation
dc.relationCGIAR Trust Fund
dc.relationhttps://hdl.handle.net/10568/130586
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source14
dc.source1664-462X
dc.sourceFrontiers in Plant Science
dc.source1114670
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectOptimized Soil
dc.subjectAdjusted Vegetation Index
dc.subjectHIGH-THROUGHPUT PHENOTYPING
dc.subjectSATELLITES
dc.subjectWHEAT
dc.subjectMAIZE
dc.subjectBREEDING
dc.subjectNORMALIZED DIFFERENCE VEGETATION INDEX
dc.subjectWheat
dc.titleSatellite imagery for high-throughput phenotyping in breeding plots
dc.typeArticle
dc.typePublished Version
dc.coverageSwitzerland


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