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APA citation
Pinto Espinosa, F., Zaman-Allah, M., Reynolds, M. P., Schulthess, U. (2023). Satellite imagery for high-throughput phenotyping in breeding plots. Frontiers in Plant Science, 14, 1114670. https://doi.org/10.3389/fpls.2023.1114670
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Advances 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.
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Journal
Frontiers in Plant Science
Journal volume
14
Journal issue
Article number
1114670
Place of Publication
Switzerland
Publisher
Frontiers
Related Datasets
CGIAR Initiatives
Initiative
Accelerated Breeding
Digital Innovation
Digital Innovation
Impact Area
Climate adaptation & mitigation
Environmental health & biodiversity
Nutrition, health & food security
Environmental health & biodiversity
Nutrition, health & food security
Action Area
Resilient Agrifood Systems
Genetic Innovation
Genetic Innovation
Donor or Funder
CGIAR Trust Fund