Unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize
Background: Recent developments in unmanned aerial platforms (UAP) have provided research opportunities in assessing land allocation and crop physiological traits, including response to abiotic and biotic stresses. UAP-based remote sensing can be used to rapidly and cost-effectively phenotype large numbers of plots and field trials in a dynamic way using time series. This is anticipated to have tremendous implications for progress in crop genetic improvement. Results: We present the use of a UAP equipped with sensors for multispectral imaging in spatial field variability assessment and phenotyping for low-nitrogen (low-N) stress tolerance in maize. Multispectral aerial images were used to (1) characterize experimental fields for spatial soil-nitrogen variability and (2) derive indices for crop performance under low-N stress. Overall, results showed that the aerial platform enables to effectively characterize spatial field variation and assess crop performance under low-N stress. The Normalized Difference Vegetation Index (NDVI) data derived from spectral imaging presented a strong correlation with ground-measured NDVI, crop senescence index and grain yield. Conclusion: This work suggests that the aerial sensing platform designed for phenotyping studies has the potential to effectively assist in crop genetic improvement against abiotic stresses like low-N provided that sensors have enough resolution for plot level data collection. Limitations and future potential uses are also discussed.
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BioMed Central
2015
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Phenotyping Platforms, UAP, MAIZE, PHENOTYPES, REMOTE SENSING, UNMANNED AERIAL VEHICLES, NITROGEN FERTILIZERS, |
Online Access: | http://hdl.handle.net/10883/16941 |
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dig-cimmyt-10883-169412024-03-13T19:38:57Z Unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize Zaman-Allah, M. Vergara Diaz, O. Araus, J.L. Amsal Tesfaye Tarekegne Magorokosho, C. Zarco-Tejada, P.J. Hornero, A. Hernández-Alba, A. Das, B. Craufurd, P. Olsen, M. Prasanna, B.M. Cairns, J.E. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Phenotyping Platforms UAP MAIZE PHENOTYPES REMOTE SENSING UNMANNED AERIAL VEHICLES NITROGEN FERTILIZERS Background: Recent developments in unmanned aerial platforms (UAP) have provided research opportunities in assessing land allocation and crop physiological traits, including response to abiotic and biotic stresses. UAP-based remote sensing can be used to rapidly and cost-effectively phenotype large numbers of plots and field trials in a dynamic way using time series. This is anticipated to have tremendous implications for progress in crop genetic improvement. Results: We present the use of a UAP equipped with sensors for multispectral imaging in spatial field variability assessment and phenotyping for low-nitrogen (low-N) stress tolerance in maize. Multispectral aerial images were used to (1) characterize experimental fields for spatial soil-nitrogen variability and (2) derive indices for crop performance under low-N stress. Overall, results showed that the aerial platform enables to effectively characterize spatial field variation and assess crop performance under low-N stress. The Normalized Difference Vegetation Index (NDVI) data derived from spectral imaging presented a strong correlation with ground-measured NDVI, crop senescence index and grain yield. Conclusion: This work suggests that the aerial sensing platform designed for phenotyping studies has the potential to effectively assist in crop genetic improvement against abiotic stresses like low-N provided that sensors have enough resolution for plot level data collection. Limitations and future potential uses are also discussed. 2016-06-13T16:05:34Z 2016-06-13T16:05:34Z 2015 Article http://hdl.handle.net/10883/16941 10.1186/s13007-015-0078-2 English CIMMYT 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. Open Access PDF ETHIOPIA London, United Kingdom BioMed Central 1 11 Plant Methods 35 |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Phenotyping Platforms UAP MAIZE PHENOTYPES REMOTE SENSING UNMANNED AERIAL VEHICLES NITROGEN FERTILIZERS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Phenotyping Platforms UAP MAIZE PHENOTYPES REMOTE SENSING UNMANNED AERIAL VEHICLES NITROGEN FERTILIZERS |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Phenotyping Platforms UAP MAIZE PHENOTYPES REMOTE SENSING UNMANNED AERIAL VEHICLES NITROGEN FERTILIZERS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Phenotyping Platforms UAP MAIZE PHENOTYPES REMOTE SENSING UNMANNED AERIAL VEHICLES NITROGEN FERTILIZERS Zaman-Allah, M. Vergara Diaz, O. Araus, J.L. Amsal Tesfaye Tarekegne Magorokosho, C. Zarco-Tejada, P.J. Hornero, A. Hernández-Alba, A. Das, B. Craufurd, P. Olsen, M. Prasanna, B.M. Cairns, J.E. Unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize |
description |
Background: Recent developments in unmanned aerial platforms (UAP) have provided research opportunities in assessing land allocation and crop physiological traits, including response to abiotic and biotic stresses. UAP-based remote sensing can be used to rapidly and cost-effectively phenotype large numbers of plots and field trials in a dynamic way using time series. This is anticipated to have tremendous implications for progress in crop genetic improvement. Results: We present the use of a UAP equipped with sensors for multispectral imaging in spatial field variability assessment and phenotyping for low-nitrogen (low-N) stress tolerance in maize. Multispectral aerial images were used to (1) characterize experimental fields for spatial soil-nitrogen variability and (2) derive indices for crop performance under low-N stress. Overall, results showed that the aerial platform enables to effectively characterize spatial field variation and assess crop performance under low-N stress. The Normalized Difference Vegetation Index (NDVI) data derived from spectral imaging presented a strong correlation with ground-measured NDVI, crop senescence index and grain yield. Conclusion: This work suggests that the aerial sensing platform designed for phenotyping studies has the potential to effectively assist in crop genetic improvement against abiotic stresses like low-N provided that sensors have enough resolution for plot level data collection. Limitations and future potential uses are also discussed. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Phenotyping Platforms UAP MAIZE PHENOTYPES REMOTE SENSING UNMANNED AERIAL VEHICLES NITROGEN FERTILIZERS |
author |
Zaman-Allah, M. Vergara Diaz, O. Araus, J.L. Amsal Tesfaye Tarekegne Magorokosho, C. Zarco-Tejada, P.J. Hornero, A. Hernández-Alba, A. Das, B. Craufurd, P. Olsen, M. Prasanna, B.M. Cairns, J.E. |
author_facet |
Zaman-Allah, M. Vergara Diaz, O. Araus, J.L. Amsal Tesfaye Tarekegne Magorokosho, C. Zarco-Tejada, P.J. Hornero, A. Hernández-Alba, A. Das, B. Craufurd, P. Olsen, M. Prasanna, B.M. Cairns, J.E. |
author_sort |
Zaman-Allah, M. |
title |
Unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize |
title_short |
Unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize |
title_full |
Unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize |
title_fullStr |
Unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize |
title_full_unstemmed |
Unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize |
title_sort |
unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize |
publisher |
BioMed Central |
publishDate |
2015 |
url |
http://hdl.handle.net/10883/16941 |
work_keys_str_mv |
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