A noninvasive, machine learning–based method for monitoring anthocyanin accumulation in plants using digital color imaging. (10th November 2019)

• Record Type: Journal Article
• Authors: Askey, Bryce C.; Dai, Ru; Lee, Won Suk; Kim, Jeongim
• Is Part Of: Applications in plant sciences. Volume 7:Number 11(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-10
• Subjects: anthocyanin -- digital color imaging -- early stress detection -- machine learning; Plants -- Periodicals; Plant physiology -- Periodicals; Plant Physiological Phenomena; Plant physiology; Plants; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 580
• DOI: 10.1002/aps3.11301
• Languages: English
• ISSNs: 2168-0450

Abstract

Abstract : Premise: 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. 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. Conclusions: 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.