A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization
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 leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R2~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.
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| Language: | English |
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Frontiers Media
2016-05-18
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| Subjects: | Breeding, Crop management, Field phenotyping, Nitrogen fertilization, NDVI, RGB indices, |
| Online Access: | http://hdl.handle.net/10261/157562 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100005774 |
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dig-ias-es-10261-1575622021-12-27T15:54:23Z A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization Vergara-Díaz, Omar Zaman-Allah, Mainassara Masuka, Benhildah Hornero, Alberto Zarco-Tejada, Pablo J. Prasanna, Boddupalli M. Cairns, Jill E. Araus, José Luis Ministerio de Economía y Competitividad (España) Universidad de Barcelona Breeding Crop management Field phenotyping Nitrogen fertilization NDVI RGB indices 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 leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R2~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization. This article was supported by grants from the MAIZE CGIAR Research Program and the Project AGL2013-44147-R from the Ministerio de Economy Competitividad of the Spanish Government. OV is a recipient of a research grant (APIF) sponsored by the University of Barcelona. Peer reviewed 2017-11-22T10:52:02Z 2017-11-22T10:52:02Z 2016-05-18 artículo http://purl.org/coar/resource_type/c_6501 Frontiers in Plant Science 7: 666 (2016) 1664-462X http://hdl.handle.net/10261/157562 10.3389/fpls.2016.00666 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100005774 27242867 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2013-44147-R Publisher's version http://doi.org/10.3389/fpls.2016.00666 Sí open Frontiers Media |
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Breeding Crop management Field phenotyping Nitrogen fertilization NDVI RGB indices Breeding Crop management Field phenotyping Nitrogen fertilization NDVI RGB indices |
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Breeding Crop management Field phenotyping Nitrogen fertilization NDVI RGB indices Breeding Crop management Field phenotyping Nitrogen fertilization NDVI RGB indices Vergara-Díaz, Omar Zaman-Allah, Mainassara Masuka, Benhildah Hornero, Alberto Zarco-Tejada, Pablo J. Prasanna, Boddupalli M. Cairns, Jill E. Araus, José Luis A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization |
| description |
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 leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R2~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization. |
| author2 |
Ministerio de Economía y Competitividad (España) |
| author_facet |
Ministerio de Economía y Competitividad (España) Vergara-Díaz, Omar Zaman-Allah, Mainassara Masuka, Benhildah Hornero, Alberto Zarco-Tejada, Pablo J. Prasanna, Boddupalli M. Cairns, Jill E. Araus, José Luis |
| format |
artículo |
| topic_facet |
Breeding Crop management Field phenotyping Nitrogen fertilization NDVI RGB indices |
| author |
Vergara-Díaz, Omar Zaman-Allah, Mainassara Masuka, Benhildah Hornero, Alberto Zarco-Tejada, Pablo J. Prasanna, Boddupalli M. Cairns, Jill E. Araus, José Luis |
| author_sort |
Vergara-Díaz, Omar |
| title |
A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization |
| title_short |
A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization |
| title_full |
A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization |
| title_fullStr |
A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization |
| title_full_unstemmed |
A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization |
| title_sort |
novel remote sensing approach for prediction of maize yield under different conditions of nitrogen fertilization |
| publisher |
Frontiers Media |
| publishDate |
2016-05-18 |
| url |
http://hdl.handle.net/10261/157562 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100005774 |
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| _version_ |
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