Quantitative evaluation of flower colour pattern by image analysis and principal component analysis of Primula sieboldii E. Morren

In Primula sieboldii E. Morren, many cultivars have been bred with the purpose of obtaining various petal colour patterns. Colour pattern is an important breeding target in this species, and the availability of an objective and quantitative evaluation method is of vital importance for both genetic analysis and variety registration. Our objective was to establish a new quantitative evaluation method of P. sieboldii flower colour patterns in regions of interest (ROIs) by principal component analysis (PCA). We first set a ROI in each petal as a region that represented the petal colour pattern and defined the maximum square on each petal as the ROI. We then converted each ROI image to a 10 × 10 pixel mosaic image and defined a total of 300 variables (the colour values for red, green and blue components of the 100 pixels) per mosaic image. Finally, we summarized the information on the 300 variables by PCA, and redrew the mosaic images to correspond to some typical principal component scores to determine the effect of each principal component on colour pattern. By this method, we detected five different features of petal colour pattern, four of which were revealed to be mainly genetically controlled. Thus, we successfully established a procedure for evaluating petal colour patterns in P. sieboldii cultivars. This new procedure can be used as a basis for an objective and efficient variety registration system.