Journal article

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Omar Vergara-Diaz, Mainassara A Zaman-Allah, Benhildah Masuka, Alberto Hornero, Pablo Zarco-Tejada, Boddupalli M Prasanna, Jill E Cairns, Jose L Araus



Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other lea..

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University of Melbourne Researchers


Awarded by Ministerio de Economia y Competitividad of the Spanish Government

Funding Acknowledgements

This article was supported by grants from the MAIZE CGIAR Research Program and the Project AGL2013-44147-R from the Ministerio de Economia y Competitividad of the Spanish Government. OV is a recipient of a research grant (APIF) sponsored by the University of Barcelona. We thank the personnel from the CIMMYT Southern Africa Regional Office at Harare for their support during the field measurements and sampling. The trials were planted under the Bill and Melinda Gates funded project Improved Maize for Africa Soils. Finally we thank Dr. Jaume Casadesus for providing the BreedPix software.