Digital photography provides a fast, reliable, and noninvasive method to estimate anthocyanin pigment concentration in reproductive and vegetative plant tissues

Abstract

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 r 2 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.

The appearance of mimetic Heliconius butterflies to predators and conspecifics

“…In fact, it has been shown to produce more accurate data than spectrometry in recent work (del Valle et al. ) and modeling of animal receptor responses is highly accurate with appropriate camera methods compared to spectrometry (Stevens and Cuthill ; Pike ; Troscianko and Stevens ). However, we emphasize that these models need to be fully tested in the future using behavioral data to determine whether an observer can effectively discriminate pairs of colors in a manner as predicted by the models.…”

“…Photography is often better in controlling for light conditions, accounting for larger color areas and spatial variation, and for angles of measurements than spectrometry, because it captures the whole scene (Lovell et al 2005;Spottiswoode and Stevens 2010;Stevens et al 2014). In fact, it has been shown to produce more accurate data than spectrometry in recent work (del Valle et al 2018) and modeling of animal receptor responses is highly accurate with appropriate camera methods compared to spectrometry (Stevens and Cuthill 2006;Pike 2011;Troscianko and Stevens 2015). However, we emphasize that these models need to be fully tested in the future using behavioral data to determine whether an observer can effectively discriminate pairs of colors in a manner as predicted by the models.…”

aradeepopsis: From images to phenotypic traits using deep transfer learning

“…Next, we used the segmentation masks obtained from araDEEPopsis to extract morphometric and color index traits of the plant (Fig 4), using the python library scikit-image [18,19]). For example, major and minor axes of an object-surrounding ellipse are a measure of the aspect ratio of the plant and inform on whether the rosette is rather round or rather elongated along one axis.…”

“…Simple color index traits are, for example, the average intensity of the green channel over all pixels classified as 'plant'. We have implemented the color index traits described by [18] in our pipeline ( Fig 4C). These traits are calculated for each class individually and for a compound class termed "plant_region", which contains both class_norm and class_antho (only for model C).…”

“…Instead of morphometric traits such as size and growth rate, which are usually highly polygenic and therefore difficult to test, we wondered if we could make use of additional layers of image information provided by araDEEPopsis as a proxy for physiological parameters. We hypothesized that RGB color information of the plant, i.e., the respective pixel intensities in the three color channels, would provide information on anthocyanin content and hence on the physiological state of the plant; it was previously shown that tissue color highly correlates with anthocyanin content [18]. Using the araDEEPopsis segmentations, which accurately reflect the area covered by the rosette, we extracted color information by collecting data from the different RGB channels from the segmented object area, as explained above (Fig 4B,C).…”