Evaluating and Mapping Grape Color Using Image-Based Phenotyping

Abstract

<jats:p>Grape berry color is an economically important trait that is controlled by two major genes influencing anthocyanin synthesis in the skin. Color is often described qualitatively using six major categories; however, this is a subjective rating that often fails to describe variation within these six classes. To investigate minor genes influencing berry color, image analysis was used to quantify berry color using different color spaces. An image analysis pipeline was developed and utilized to quantify color in a segregating hybrid wine grape population across two years. Images were collected from grape clusters immediately after harvest and segmented by color to determine the red, green, and blue (RGB); hue, saturation, and intensity (HSI); and lightness, red-green, and blue-yellow values (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mtext>L</mml:mtext><mml:mo>∗</mml:mo><mml:mtext>a</mml:mtext><mml:mo>∗</mml:mo><mml:mtext>b</mml:mtext><mml:mo>∗</mml:mo></mml:math>) of berries. QTL analysis identified known major QTL for color on chromosome 2 along with several previously unreported smaller-effect QTL on chromosomes 1, 5, 6, 7, 10, 15, 18, and 19. This study demonstrated the ability of an image analysis phenotyping system to characterize berry color and to more effectively capture variability within a population and identify genetic regions of interest.</jats:p>

Image-based Phenotyping Identifies Quantitative Trait Loci for Cluster Compactness in Grape

“…Assessments were performed on fruit clusters produced in 2017 and 2018. The harvest process is the same as that described by Underhill et al (2020); briefly, three clusters from each GE1025 genotype were collected at maturity (soluble solids concentration >20%) for laboratory image collection. The fruit colors present in GE1025 can also be found in detail in the work by Underhill et al (2020).…”

“…IMAGE COLLECTION. The image collection and segmentation process described here and the equipment specifications have been detailed by Underhill et al (2020). Each cluster was suspended by a clip from its rachis, and four digital images were obtained of each cluster with 90°rotations between each to produce 12 images per genotype.…”

“…IMAGE SEGMENTATION. A detailed description of the segmentation process can be found in the work by Underhill et al (2020). Food Color Inspector software (Albert et al, 2013) was used for segmentation, which uses a semi-automated process requiring the user to select 3-• 3-pixel squares to define a training set for each color class (berry, rachis, or background).…”

“…A diagram of the camera, lighting, and backdrop setup for grape cluster image capture (not to scale). Grape clusters were suspended by an alligator clip in front of a flat white background and illuminated with a fluorescent light(Underhill et al, 2020).…”

Quantitative dissection of color patterning in the foliar ornamental Coleus reveals underlying features driving aesthetic value

“…Indeed, the majority of color phenotypes expressed in plants are typically uniformly expressed (for example, monochromatic leaves and berries (Underhill et al, 2020)), un-patterned in their expression (for example, lesions (Arnal Barbedo, 2013;Gobalakrishnan et al, 2020;Xie et al, 2020)), or have highly predictable patterns (for example, nectar guides). These color phenotypes are readily extractable using existing image processing approaches that are not suited for the complex suite of color patterns represented in our coleus population (Arnal Barbedo, 2013;Gobalakrishnan et al, 2020;Xie et al, 2020)).…”