<title>Line-scan inspection of conifer seedlings</title>

Abstract

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 (r<SUP>2</SUP> equals 0.90) with shoot volume. Projected root area had good correlation (r<SUP>2</SUP> equals 0.80) with root volume. Seedlings were inspected at rates as high as ten per second.