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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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Abstract
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.
Keywords
Agronomy and Crop Science, Forestry, Horticulture, Computer Science Applications

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Chicago
Asaari, Mohd Shahrimie Mohd, Stien Mertens, Stijn Dhondt, Dirk Inzé, Nathalie Wuyts, and Paul Scheunders. 2019. “Analysis of Hyperspectral Images for Detection of Drought Stress and Recovery in Maize Plants in a High-throughput Phenotyping Platform.” Computers and Electronics in Agriculture 162: 749–758.
APA
Asaari, M. S. M., Mertens, S., Dhondt, S., Inzé, D., Wuyts, N., & Scheunders, P. (2019). Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform. Computers and Electronics in Agriculture, 162, 749–758.
Vancouver
1.
Asaari MSM, Mertens S, Dhondt S, Inzé D, Wuyts N, Scheunders P. Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform. Computers and Electronics in Agriculture. Elsevier BV; 2019;162:749–58.
MLA
Asaari, Mohd Shahrimie Mohd et al. “Analysis of Hyperspectral Images for Detection of Drought Stress and Recovery in Maize Plants in a High-throughput Phenotyping Platform.” Computers and Electronics in Agriculture 162 (2019): 749–758. Print.
@article{8616844,
  abstract     = {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.},
  author       = {Asaari, Mohd Shahrimie Mohd and Mertens, Stien and Dhondt, Stijn and Inz{\'e}, Dirk and Wuyts, Nathalie and Scheunders, Paul},
  issn         = {0168-1699},
  journal      = {Computers and Electronics in Agriculture},
  pages        = {749--758},
  publisher    = {Elsevier BV},
  title        = {Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform},
  url          = {http://dx.doi.org/10.1016/j.compag.2019.05.018},
  volume       = {162},
  year         = {2019},
}

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