MOESM3 of Computer vision and machine learning enabled soybean root phenotyping pipeline

Additional file 3: Table S1. A list of imaging system components. Table S2. Validation correlations between 23 RSA traits extracted from ARIA 2.0 using 298 heuristically segmented and CAE segmented root images. Table S3. RSA traits at 6d, 9d and 12d when grouped into country of origin, growth habit type and diversity of genetic background. Table S4. Descriptive statistics for 6 root and shoot traits of 115 maturity group II genotypes of soybean at 6d, 9d and 12d, obtained from BLUP values for each genotype using heuristically segmented images. Table S5. Validation correlation between GiARoot software and ARIA 2.0. Table S6. Minimal root angle diversity among the three genotypes. A Kolmogorov-Smirnov test was used to detect p-value statistical differences in directionality on root branching angle at each of the three time points. Table S7. Correlations between plant dry weight taken at 12d and root traits at 9d for 115 maturity group II genotypes of soybean.