Extraction of Quantitative Information from Hyperspectral Reflectance Images for Noninvasive Plant Phenotyping

Assessment of plant traits (phenotyping) is central to modern advanced techniques of plant sciences and accelerated breeding of crop plants, including fruit crops, for improving productivity and stress resilience. Hyperspectral reflectance imaging is an emerging method allowing to capture a vast amount of the structural, biochemical, and phenological information about plants. The advent of low-cost hyperspectrometers made this method affordable for a broad community of plant scientists. However, extraction of sensible information from reflectance images is hindered by the complexity of plant optical properties, especially when they are measured in the field. We propose using reflectance indices (Plant Senescence Reflectance Index, PSRI; Anthocyanin Reflectance Index, ARI; and spectral deconvolution) previously developed for remote sensing of vegetation and point-based reflectometers to infer the spatially resolved information on plant development and biochemical composition using lettuce (Lactuca sativa L.) leaves and ripening apple (Malus × domestica Borkh.) fruit as the model. Specifically, the proposed approach enables capturing data on distribution of chlorophylls and primary carotenoids as well as secondary carotenoids (both linked with fruit ripening and leaf senescence during plant development) as well as the information on spatial distribution of anthocyanins (known as stress pigments) over the plant surface. We argue that the proposed approach would enrich the phenotype assessments made on the base of reflectance image analysis with valuable information on plant physiological condition, stress acclimation state, and the progression of the plant development.