A noninvasive, machine learning-based method for monitoring anthocyanin accumulation in plants using digital color imaging.

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.