A noninvasive, machine learning-based method for monitoring anthocyanin accumulation in plants using digital color imaging
Askey, Dai, Lee, Kim (2019) A noninvasive, machine learning-based method for monitoring anthocyanin accumulation in plants using digital color imaging Appl Plant Sci (IF: 3.6) 7(11) e11301
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Abstract
When plants are exposed to stress conditions, irreversible damage can occur, negatively impacting yields. It is therefore important to detect stress symptoms in plants, such as the accumulation of anthocyanin, as early as possible.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.The digital imaging-based nature of this protocol makes it a low-cost and noninvasive method for the detection of plant stress. Applying a similar protocol to more economically viable crops could lead to the development of large-scale, cost-effective systems for monitoring plant health.© 2019 Askey et al. Applications in Plant Sciences is published by Wiley Periodicals, Inc. on behalf of the Botanical Society of America.
Links
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6858293http://www.ncbi.nlm.nih.gov/pubmed/31832283
http://dx.doi.org/10.1002/aps3.11301
