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

Abstract

Grape ( Vitis vinifera ) cluster compactness is an important trait due to its effect on disease susceptibility, but visual evaluation of compactness relies on human judgement and an ordinal scale that is not appropriate for all populations. We developed an image analysis pipeline and used it to quantify cluster compactness traits in a segregating hybrid wine grape ( Vitis sp.) population for 2 years. Images were collected from grape clusters immediately after harvest, segmented by color, and analyzed using a custom script. Both automated and conventional phenotyping methods were used, and comparisons were made between each method. A partial least squares (PLS) model was constructed to evaluate the prediction of physical cluster compactness using image-derived measurements. Quantitative trait loci (QTL) on chromosomes 4, 9, 12, 16, and 17 were associated with both image-derived and conventionally phenotyped traits within years, which demonstrated the ability of image-derived traits to identify loci related to cluster morphology and cluster compactness. QTL for 20-berry weight were observed between years on chromosomes 11 and 17. Additionally, the automated method of cluster length measurement was highly accurate, with a deviation of less than 10 mm ( r = 0.95) compared with measurements obtained with a hand caliper. A remaining challenge is the utilization of color-based image segmentation in a population that segregates for fruit color, which leads to difficulty in differentiating the stem from the fruit when the two are similarly colored in non-noir fruit. Overall, this research demonstrates the validity of image-based phenotyping for quantifying cluster compactness and for identifying QTL for the advancement of grape breeding efforts.