Measuring radiata pine seedling morphological features using a machine vision system

Abstract

Radiata pine seedlings are raised in nursery beds and are lifted and sorted in winter before being sold to forestry companies. This happens to order, as bare-root stock cannot survive for long out of the ground. The sorting criteria are usually clearly defined but exact measurements cannot be obtained due to the accuracy of the measuring equipment and the variability in the tree stock due to their organic nature. Machine vision is employed to measure the seedling features since it is more objective, faster, and less prone to error than manual measurements. This article presents three algorithms to measure the seedling features of root collar diameter, seedling height and root spread using a machine vision system with image processing techniques of noise removal and segmentation. The unnecessary information is removed by dilation and erosion while segmentation is based on the variances of the pixels. Processing time was approximately 30 ms for an entire seedling. The machine vision measurements are compared to the manual measurements. There is good correlation between the measurements of seedling features taken with the machine vision system and manual process: 95% of machine vision measurements for root collar diameter are within approximately 1.3 mm of manually measured values; 95% of machine vision measurements for height are within 18 mm of manual measurements; and 95% of machine vision measurements for maximum void angle of roots are within 87° less, and 78° more than manual measurements.