Extraction of Quantitative Information from Hyperspectral Reflectance Images for Noninvasive Plant Phenotypingстатья
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Дата последнего поиска статьи во внешних источниках: 26 июня 2024 г.
Аннотация:—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 stressresilience. Hyperspectral reflectance imaging is an emerging method allowing to capture a vast amount ofthe 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, extractionof 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 SenescenceReflectance Index, PSRI; Anthocyanin Reflectance Index, ARI; and spectral deconvolution) previouslydeveloped for remote sensing of vegetation and point-based reflectometers to infer the spatially resolvedinformation on plant development and biochemical composition using lettuce (Lactuca sativa L.) leavesand ripening apple (Malus × domestica Borkh.) fruit as the model. Specifically, the proposed approachenables 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 arguethat the proposed approach would enrich the phenotype assessments made on the base of reflectanceimage analysis with valuable information on plant physiological condition, stress acclimation state, and theprogression of the plant development.