Hyperspectral imaging for chloroplast movement detection

Abstract

We employed hyperspectral imaging to assess chloroplast positions in Nicotiana benthamiana and Arabidopsis thaliana leaves. 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, however, 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 differential leaf reflectance in the green-yellow region of the spectrum. High blue light irradiation caused a characteristic ‘saddle’ in the green-yellow region and a peak at around 695 nm. The obtained curves were then used for training and validation using machine learning methods, including convolutional neural networks. Low levels of misclassification in both conditions, when the same or different species were used for training, serve as a proof-of-concept for this method. We also propose a Chloroplast Movement Index for validation of chloroplast positions in leaves. Moreover, the analysis of commonly used vegetation indices shows 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.