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Genetic Dissection of Seasonal Changes in a Greening Plant Based on Time-Series Multispectral Imaging.

Taeko KojiHiroyoshi IwataMotoyuki IshimoriHideki TakanashiYuji YamasakiHisashi Tsujimoto
Published in: Plants (Basel, Switzerland) (2023)
Good appearance throughout the year is important for perennial ornamental plants used for rooftop greenery. However, the methods for evaluating appearance throughout the year, such as plant color and growth activity, are not well understood. In this study, evergreen and winter-dormant parents of Phedimus takesimensis and 94 F 1 plants were used for multispectral imaging. We took 16 multispectral image measurements from March 2019 to April 2020 and used them to calculate 15 vegetation indices and the area of plant cover. QTL analysis was also performed. Traits such as the area of plant cover and vegetation indices related to biomass were high during spring and summer (growth period), whereas vegetation indices related to anthocyanins were high in winter (dormancy period). According to the PCA, changes in the intensity of light reflected from the plants at different wavelengths over the course of a year were consistent with the changes in plant color and growth activity. Seven QTLs were found to be associated with major seasonal growth changes. This approach, which monitors not only at a single point in time but also over time, can reveal morphological changes during growth, senescence, and dormancy throughout the year.
Keyphrases
  • climate change
  • high resolution
  • genome wide
  • gene expression
  • machine learning
  • deep learning
  • dna damage
  • dna methylation
  • mass spectrometry
  • wastewater treatment
  • single cell
  • stress induced