Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform

Mohd Shahrimie Mohd Asaari*, Puneet Mishra, Stien Mertens, Stijn Dhondt, Dirk Inzé, Nathalie Wuyts, Paul Scheunders

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

39 Citations (Scopus)

Abstract

The potential of close-range hyperspectral imaging (HSI) as a tool for detecting early drought stress responses in plants grown in a high-throughput plant phenotyping platform (HTPPP) was explored. Reflectance spectra from leaves in close-range imaging are highly influenced by plant geometry and its specific alignment towards the imaging system. This induces high uninformative variability in the recorded signals, whereas the spectral signature informing on plant biological traits remains undisclosed. A linear reflectance model that describes the effect of the distance and orientation of each pixel of a plant with respect to the imaging system was applied. By solving this model for the linear coefficients, the spectra were corrected for the uninformative illumination effects. This approach, however, was constrained by the requirement of a reference spectrum, which was difficult to obtain. As an alternative, the standard normal variate (SNV) normalisation method was applied to reduce this uninformative variability. Once the envisioned illumination effects were eliminated, the remaining differences in plant spectra were assumed to be related to changes in plant traits. To distinguish the stress-related phenomena from regular growth dynamics, a spectral analysis procedure was developed based on clustering, a supervised band selection, and a direct calculation of a spectral similarity measure against a reference. To test the significance of the discrimination between healthy and stressed plants, a statistical test was conducted using a one-way analysis of variance (ANOVA) technique. The proposed analysis techniques was validated with HSI data of maize plants (Zea mays L.) acquired in a HTPPP for early detection of drought stress in maize plant. Results showed that the pre-processing of reflectance spectra with the SNV effectively reduces the variability due to the expected illumination effects. The proposed spectral analysis method on the normalized spectra successfully detected drought stress from the third day of drought induction, confirming the potential of HSI for drought stress detection studies and further supporting its adoption in HTPPP.

Original languageEnglish
Pages (from-to)121-138
Number of pages18
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume138
DOIs
Publication statusPublished - Apr 2018
Externally publishedYes

Keywords

  • Close-range hyperspectral imaging
  • Linear reflectance model
  • Plant stress
  • Spectral similarity measure
  • Standard normal variate

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