Asaari, Mohd Shahrimie Mohd

Dhondt, Stijn

Inz\ue9, Dirk

Mertens, Stien

Scheunders, Paul

Abstract: The optical signature of a plant is an essential tool in predicting vegetation water content for quantitative assessment of plant status under drought stress. Plant responses to water stress may involve optically-complex reactions, thus a more sophisticated learning algorithm is needed for accurate prediction. This study proposes Kernel Ridge Regression (KRR) algorithm, a simple yet effective nonlinear learning method to uncover the complex relationship between the response variable and input spectra. A prediction model was developed by calibrating the normalized spectral in Short-Wave-Infrared (SWIR) with the leaf Relative Water Content (RWC) values. The predicted model was applied to a time-series of Hyperspectral images (HSI) of maize plants for early detection of drought stress. RWC was estimated for every plant pixel, and the histogram representation was constructed to characterize the whole plant. Discrimination between healthy and stressed plants was achieved by means of the histogram similarity measure. Further, a OneWay Analysis of Variance (ANOVA) was applied to test the significance of the discrimination between healthy and stressed plants. The proposed method successfully detected drought stress from the fifth day of drought induction, confirming the potential of HSI for drought stress detection studies

Analysis of plant stress response using hyperspectral imaging and kernel ridge regression

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