High-throughput Phenotyping of Maize Roots Using Digital Image Analysis

Abstract

Recent research on maize root architecture has made significant progress, but further research is needed to optimize methods for efficient and accurate acquisition of root architecture data. This study aimed to assess the effectiveness of digital imaging for root phenotyping of Zea mays L. Field experiments were carried out at two locations in the province of Antioquia, Colombia, in 2019 and 2020 to analyze root architecture variables of 12 genotypes of maize. Two methodologies were used: manual phenotyping and digital image analysis. Pearson’s correlation coefficients among variables were estimated. Principal Component Analysis (PCA) was used to summarize and uncover clustering patterns in the multivariate data set. The results indicated correlations between diameter (r = 0.94) and manually measured root diameter. The manually measured right and left root angles correlated with image-derived root angle at r = 0.92 and 0.88, respectively, and root length at r = 0.62. The PCA highlighted that the digital method explained the highest proportion of variation in root areas and diameters, while the manual method dominated in root angle variables. These results corroborate a feasible method to optimize root architecture phenotyping for research questions. This protocol can be adopted under the automatic analysis with REST software for acquiring images of variables associated with roots’ angle, length, and diameter.