Applications in Plant Sciences 2019-Nov
A noninvasive, machine learning-based method for monitoring anthocyanin accumulation in plants using digital color imaging.
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Abstract
Methods and Results
Twenty-two regression models in five color spaces were trained to develop a prediction model for plant anthocyanin levels from digital color imaging data. Of these, a quantile random forest regression model trained with standard red, green, blue (sRGB) color space data most accurately predicted the actual anthocyanin levels. This model was then used to noninvasively monitor the spatial and temporal accumulation of anthocyanin in Arabidopsis thaliana leaves.