Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111757
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Type: Journal article
Title: Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging
Author: Khan, Z.
Rahimi-Eichi, V.
Haefele, S.
Garnett, T.
Miklavcic, S.
Citation: Plant Methods, 2018; 14(1):20-1-20-11
Publisher: BioMed Central
Issue Date: 2018
ISSN: 1746-4811
1746-4811
Statement of
Responsibility: 
Zohaib Khan, Vahid Rahimi-Eichi, Stephan Haefele, Trevor Garnett and Stanley J. Miklavcic
Abstract: Unmanned aerial vehicles offer the opportunity for precision agriculture to efficiently monitor agricultural land. A vegetation index (VI) derived from an aerially observed multispectral image (MSI) can quantify crop health, moisture and nutrient content. However, due to the high cost of multispectral sensors, alternate, low-cost solutions have lately received great interest. We present a novel method for model-based estimation of a VI using RGB color images. The non-linear spatio-spectral relationship between the RGB image of vegetation and the index computed by its corresponding MSI is learned through deep neural networks. The learned models can be used to estimate VI of a crop segment.Analysis of images obtained in wheat breeding trials show that the aerially observed VI was highly correlated with ground-measured VI. In addition, VI estimates based on RGB images were highly correlated with VI deduced from MSIs. Spatial, spectral and temporal information of images contributed to estimation of VI. Both intra-variety and inter-variety differences were preserved by estimated VI. However, VI estimates were reliable until just before significant appearance of senescence.The proposed approach validates that it is reasonable to accurately estimate VI using deep neural networks. The results prove that RGB images contain sufficient information for VI estimation. It demonstrates that low-cost VI measurement is possible with standard RGB cameras.
Keywords: Wheat; phenotyping; deep learning; precision agriculture
Rights: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
RMID: 0030084137
DOI: 10.1186/s13007-018-0287-6
Grant ID: http://purl.org/au-research/grants/arc/IH130200027
Appears in Collections:Agriculture, Food and Wine publications

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