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Title
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Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform
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Author
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Abstract
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| The study of physiological processes resulting from water-limited conditions in crops is essential for the selection of drought-tolerant genotypes and the functional analysis of related genes. A promising, non-invasive technique for plant trait analysis is close-range hyperspectral imaging (HSI), which has great potential for the early detection of plant responses to water deficit stress. In this work, a data analysis method is described that, unlike vegetation indices, the present method applies spectral similarity on selected bands with high discriminative information, while requiring a careful treatment of uninformative illumination effects. The latter issue is solved by a standard normal variate (SNV) normalization that removes linear effects and a supervised clustering approach to remove pixels that exhibit nonlinear multiple scattering effects. On the remaining pixels, the stress-related dynamics is quantified by a spectral analysis procedure that involves a supervised band selection procedure and a spectral similarity measure against well-watered control plants. The proposed method was validated by a large-scale study of water-stress and recovery of maize plants in a high-throughput plant phenotyping platform. The results showed that the analysis method allows for an early detection of drought stress responses and of recovery effects shortly after re-watering. |
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Language
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English
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Source (journal)
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Computers and electronics in agriculture. - Amsterdam
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Publication
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Amsterdam
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2019
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ISSN
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0168-1699
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DOI
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10.1016/J.COMPAG.2019.05.018
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Volume/pages
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162
(2019)
, p. 749-758
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ISI
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000473379500073
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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