Hyperspectral imaging for chloroplast movement detection

Abstract

Summary We employed hyperspectral imaging to detect chloroplast positioning in Nicotiana benthamiana and Arabidopsis thaliana leaves and assess its influence on commonly used vegetation indices. In low light, chloroplasts move to cell walls perpendicular to the direction of the incident light. In high light, they move to cell walls parallel to the light direction. Chloroplast movements result in significant changes in leaf transmittance and reflectance. The changes in leaf reflectance offer a way to examine chloroplast positioning in a non-contact way. At the same time, they may confound remote sensing of other physiological traits. The shape of reflectance spectra recorded on irradiated and non-irradiated parts of N. benthamiana and A. thaliana leaves indicated the specific position of chloroplasts. Low blue light resulted in a decrease in leaf reflectance in the green-yellow region of the spectrum. High blue light irradiation caused an increase in leaf reflectance in the visible range. The differential spectra, showing the effect of high light on leaf reflectance, exhibited a characteristic saddle in the green-yellow region and a peak at around 695 nm. Results obtained for A. thaliana mutants with disrupted chloroplast movements suggest that the observed spectral changes are mostly due to the chloroplast relocations. The reflectance spectra were used to train machine learning methods in the classification of leaves according to the chloroplast positioning. The convolutional network showed low levels of misclassification of leaves irradiated with high light even when different species were used for training and testing. This suggests that reflectance spectra may be used to detect the chloroplast avoidance response in heterogeneous patches of vegetation. We also examined the correlation between chloroplast positioning and values of indices of normalized-difference type for various combinations of wavelengths and proposed a chloroplast movement index for validation of chloroplast positions in leaves. The analysis of commonly used vegetation indices showed that their values may be altered due to chloroplast rearrangements. Our work indicates that changes in leaf reflectance due to chloroplast movements may be substantial and should be taken into account in remote sensing studies.

Plant phenotyping relevance

ハイパースペクトル反射を用いて葉内の葉緑体位置を非接触検出し、機械学習分類と新しい指標を提案・検証しており、表現型取得手法が研究の中心です。

Code and data availability

The supplied blocks describe hyperspectral imaging, machine learning classification, and a Chloroplast Movement Index, but contain no data availability statement, deposited dataset, public repository, or author code URL. The only URLs present are literature citations, and the analysis was done in Mathematica with no de

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