PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging

Chinese cabbage (Brassica rapa L. ssp. pekinensis), a leafy vegetable, exhibits a range of leaf colors, with the dark green varieties being favored by consumers. Manual visual identification of Chinese cabbage leaf color phenotypes is subjective and it is difficult to distinguish between subtle differences in leaf color, posing challenges for precision breeding. In this study, we constructed a partial least squares discriminant analysis (PLS-DA) leaf color identification model and compared four classification methods for leaf color, namely red, green, and blue (RGB) channels, hue, saturation, and lightness (HSL) color space, multi-spectrum and data-fusion. The PLS-DA supervised leaf color phenotype identification model based on data fusion can improve the recognition rate by 1%−13% compared to a single spectral model. To further validate the model, we conducted a bulked segregant analysis (BSA) of a mixed pool of a Chinese cabbage F₂ population (F₂-449) using whole-genome sequencing. The candidate locus related to dark green leaf color was reduced by 9.76 Mb compared to the manual visual inspection which provides convenience for the localization of candidate genes. Therefore, the development of a precise phenotypic identification system for Chinese cabbage that can distinguish subtle leaf color differences using high-throughput phenotype analysis technology is of great significance and agricultural practical value for the mining of high-throughput genomic data.