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A machine vision and handling system for measurement of containerized tree seedlings

University of British Columbia

The commercial forest industry in British Columbia, along with the Ministry of Forests and private nurseries, produce over two hundred million containerized tree seedlings annually. At harvest, these seedlings are lifted out of their growing container and graded to remove inferior stock based on their dimensions and appearance. Manual grading is a labor-intensive and costly operation. The work is subjective and susceptible to human error. Advances in the capability and a parallel decline in cost of machine vision hardware have spurred interest in automated grading as an alternative. A PC-based machine vision and handling system providing rapid measurement of containerized seedling morphological features has been developed in this research. The system, which employs a novel design of line-scan imaging using fiber optics in conjunction with structured backlight, addresses the production needs of commercial forest nurseries. The multiple processing approach of simultaneously scanning an entire row of 14 seedlings satisfies the time constraint of 8 seconds to measure the stem diameter and shoot height of all seedlings based on their high contrast images projected onto the optical fiber sensors. The system has a high transverse resolution equal to the 0.25 mm diameter of the optical fibers for precise measurement of stem diameter. Designed for on-line inspection, the system also provides a multi-functional, menu-driven graphical user interface. The interface offers a convenient and interactive means to control the scanning operation and adapt to various grading criteria. In addition, it supports different scanning and display capabilities, and produces simple statistics for each measured feature which would be valuable for quality control and quality improvement purposes. Tests were conducted to evaluate the performance of the system using different seedling samples and a test object of known size representing a seedling. Average classification error of tree seedlings was found to be 9%. The accuracy of diameter measurement had a low standard deviation of 0.12 mm. The speed of scanning depended upon the desired accuracy of the length measurement. At a belt speed of 4 cm/s, the accuracy of length measurement was found to be 0.6 cm with a corresponding seedling throughput of 1.75 seedlings per second.

Thesis/Dissertation

application/pdf

2009-04-30

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http://hdl.handle.net/2429/7774

Mechanical Engineering

University of British Columbia

UBCV

DSpace