Satellite imagery for high-throughput phenotyping in breeding plots

Francisco Pinto; Mainassara Zaman-Allah; Matthew Reynolds; Urs Schulthess

doi:10.3389/fpls.2023.1114670

Satellite imagery for high-throughput phenotyping in breeding plots

Front Plant Sci. 2023 May 16:14:1114670. doi: 10.3389/fpls.2023.1114670. eCollection 2023.

Authors

Francisco Pinto¹, Mainassara Zaman-Allah², Matthew Reynolds¹, Urs Schulthess³

Affiliations

¹ Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
² Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Southern Africa Regional Office, Harare, Zimbabwe.
³ CIMMYT-China Wheat and Maize Joint Research Center, Agronomy College, Henan Agricultural University, Zhengzhou, China.

Abstract

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.

Keywords: breeding; high-throughput phenotyping; maize; normalized difference vegetation index; optimized soil adjusted vegetation index; satellite; wheat.

Grants and funding

Foundation for Food and Agriculture Research: funded the field trials in wheat through the grant ID DFs-19-0000000013. CGIAR Research Program on Maize: funded purchase of satellite images. CGIAR Research Program on Wheat: funded purchase of satellite images. One CGIAR Digital Innovation Initiatives. One CGIAR F2R-CWANA Initiative. One CGIAR Accelerated Breeding Initiative.