Hyperspectral imaging for chloroplast movement detection.

We employed hyperspectral imaging to detect chloroplast positioning and assess its influence on common vegetation indices. In low blue light, chloroplasts move to cell walls perpendicular to the direction of the incident light. In high blue light, chloroplasts exhibit the avoidance response, moving to cell walls parallel to the light direction. Irradiation with high light resulted in significant changes in leaf reflectance and the shape of the reflectance spectrum. Using mutants with disrupted chloroplast movements, we found that blue light-induced changes in the reflectance spectrum are mostly due to chloroplast relocations. We trained machine learning methods in the classification of leaves according to the chloroplast positioning, based on the reflectance spectra. The convolutional network showed low levels of misclassification of leaves irradiated with high light even when different species were used for training and testing, suggesting that reflectance spectra may be used to detect chloroplast avoidance in heterogeneous vegetation. We also examined the correlation between chloroplast positioning and values of indices of normalized-difference type for various combinations of wavelengths and identified an index sensitive to chloroplast positioning. We found that values of some of the vegetation indices, including those sensitive to the carotenoid levels, may be altered due to chloroplast rearrangements.