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

• Published in: Computers and electronics in agriculture Vol. 162; pp. 749 - 758
• Main Authors: Asaari, Mohd Shahrimie Mohd, Mertens, Stien, Dhondt, Stijn, Inzé, Dirk, Wuyts, Nathalie, Scheunders, Paul
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2019; Elsevier BV
• Subjects: Close-range hyperspectral imaging; Clustering; Corn; Data analysis; Drought; Drought stress; Functional analysis; High-throughput plant phenotyping; Hyperspectral imaging; Image detection; Pixels; Recovery; Similarity; Spectra; Spectral similarity measure; Spectrum analysis; Drought stress; Spectral similarity measure; High-throughput plant phenotyping; Clustering; Close-range hyperspectral imaging
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2019.05.018

•The use of the standard normal variate (SNV) to eliminate linear effects.•The use of a clustering approach to remove pixels that exhibit nonlinear effects.•The development of a data-driven spectral analysis method to charecterize plant growth dynamics.•The validation the proposed method by a large-scale study of water-stress and recovery of maize plants. 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.