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Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform

Asaari, Mohd Shahrimie Mohd, Mertens, Stien, Dhondt, Stijn, Inzé, Dirk, Wuyts, Nathalie, Scheunders, Paul
Computers and electronics in agriculture 2019 v.162 pp. 749-758
Zea mays, corn, crops, drought tolerance, genes, genotype, hyperspectral imagery, lighting, phenotype, plant response, stress response, vegetation index, water stress
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.