Almost two billion conifer seedlings are produced in the U.S. each year to support reforestation efforts. Seedlings are graded manually to improve viability after transplanting. Manual grading is labor-intensive and subject to human variability. Our previous research demonstrated the feasibility of automated tree seedling inspection with machine vision. Here we describe a system based on line-scan imaging, providing a three-fold increase in resolution and inspection rate. A key aspect of the system is automatic recognition of the seedling root collar. Root collar diameter, shoot height, and projected shoot and root areas are measured. Sturdiness ratio and shoot/root ratio are computed. Grade is determined by comparing measured features with pre-defined set points. Seedlings are automatically sorted. The precision of machine vision and manual measurements was determined in tests at a commercial forest nursery. Manual measurements of stem diameter, shoot height, and sturdiness ratio had standard deviations three times those of machine vision measurements. Projected shoot area was highly correlated (r2 equals 0.90) with shoot volume. Projected root area had good correlation (r2 equals 0.80) with root volume. Seedlings were inspected at rates as high as ten per second.