ArticleApplications in plant sciences2019
A noninvasive, machine learning-based method for monitoring anthocyanin accumulation in plants using digital color imaging.
Article in Applications in plant sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning and digital imaging for spatiotemporal monitoring of stress dynamics in the clonal plant Carpobrotus edulis: uncovering a functional mosaic.Annals of botany · 2025Article
- Two-fold red excess (TREx): a simple and novel digital color index that enables non-invasive real-time monitoring of green-leaved as well as anthocyanin-rich crops.Plant methods · 2025Article
- Assessing nutritional pigment content of green and red leafy vegetables by image analysis: Catching the "red herring" of plant digital color processing via machine learning.Biology methods & protocols · 2025Article
- Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation.Nature communications · 2023Article
- Image-based phenotyping to estimate anthocyanin concentrations in lettuce.Frontiers in plant science · 2023Article
- Native ectomycorrhizal fungi from the endangered pine rocklands are superior symbionts to commercial inoculum for slash pine seedlings.Mycorrhiza · 2022Article
- A noninvasive, machine learning-based method for monitoring anthocyanin accumulation in plants using digital color imaging.Applications in plant sciences · 2019Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseWhen 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
resultsTwenty-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
conclusionsThe 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.
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