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

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.

SUBMITTER: Askey BC

PROVIDER: S-EPMC6858293 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Askey Bryce C BC Dai Ru R Lee Won Suk WS Kim Jeongim J

Applications in plant sciences 20191110 11

<h4>Premise</h4>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.<h4>Methods and results</h4>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 stand ...[more]

PMID: 31832283

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Project description:Anthocyanin pigments have become a model trait for evolutionary ecology as they often provide adaptive benefits for plants. Anthocyanins have been traditionally quantified biochemically or more recently using spectral reflectance. However, both methods require destructive sampling and can be labor intensive and challenging with small samples. Recent advances in digital photography and image processing make it the method of choice for measuring color in the wild. Here, we use digital images as a quick, noninvasive method to estimate relative anthocyanin concentrations in species exhibiting color variation. Using a consumer-level digital camera and a free image processing toolbox, we extracted RGB values from digital images to generate color indices. We tested petals, stems, pedicels, and calyces of six species, which contain different types of anthocyanin pigments and exhibit different pigmentation patterns. Color indices were assessed by their correlation to biochemically determined anthocyanin concentrations. For comparison, we also calculated color indices from spectral reflectance and tested the correlation with anthocyanin concentration. Indices perform differently depending on the nature of the color variation. For both digital images and spectral reflectance, the most accurate estimates of anthocyanin concentration emerge from anthocyanin content-chroma ratio, anthocyanin content-chroma basic, and strength of green indices. Color indices derived from both digital images and spectral reflectance strongly correlate with biochemically determined anthocyanin concentration; however, the estimates from digital images performed better than spectral reflectance in terms of r2 and normalized root-mean-square error. This was particularly noticeable in a species with striped petals, but in the case of striped calyces, both methods showed a comparable relationship with anthocyanin concentration. Using digital images brings new opportunities to accurately quantify the anthocyanin concentrations in both floral and vegetative tissues. This method is efficient, completely noninvasive, applicable to both uniform and patterned color, and works with samples of any size.

Project description:BACKGROUND:Anthocyanins are the primary source of colour in flowers and also accumulate in vegetative tissues, where they have multiple protective roles traditionally attributed to early compounds of the metabolic pathway (flavonols, flavones, etc.). Petal-specific loss of anthocyanins in petals allows plants to escape from the negative pleiotropic effects of flavonoid and anthocyanins loss in vegetative organs, where they perform a plethora of essential functions. Herein, we investigate the degree of pleiotropy at the biochemical scale in a pink-white flower colour polymorphism in the shore campion, Silene littorea. We report the frequencies of pink and white individuals across 21 populations and underlying biochemical profiles of three flower colour variants: anthocyanins present in all tissues (pink petals), petal-specific loss of anthocyanins (white petals), and loss of anthocyanins in all tissues (white petals). RESULTS:Individuals lacking anthocyanins only in petals represent a stable polymorphism in two populations at the northern edge of the species range (mean frequency 8-21%). Whereas, individuals lacking anthocyanins in the whole plant were found across the species range, yet always at very low frequencies (<?1%). Biochemically, the flavonoids detected were anthocyanins and flavones; in pigmented individuals, concentrations of flavones were 14-56× higher than anthocyanins across tissues with differences of >?100× detected in leaves. Loss of anthocyanin pigmentation, either in petals or in the whole plant, does not influence the ability of these phenotypes to synthesize flavones, and this pattern was congruent among all sampled populations. CONCLUSIONS:We found that all colour variants showed similar flavone profiles, either in petals or in the whole plant, and only the flower colour variant with anthocyanins in photosynthetic tissues persists as a stable flower colour polymorphism. These findings suggest that anthocyanins in photosynthetic tissues, not flavonoid intermediates, are the targets of non-pollinator mediated selection.

Project description:With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.

Dataset Information

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

Publications

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

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