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Simplified No-Code Machine Vision Method of Accurate Dimension Measurement of Granular Materials

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  RRV12124,  ASABE/CSBE North Central Intersectional Meeting. (doi: 10.13031/2013.41317) @2007
Authors:   Igathinathane - Cannayen, Leslie F Backer
Keywords:   ImageJ, particles, plugin, dimensions, physical properties, image processing

Dimension measurement of agricultural produce and products plays a vital role in quality control, grading, and characterization. Measurement using digital calipers is the most common manual method, but the difficulty increases especially when the particles size is small or their number is large. Useful measurements, such as length, width, projected area, and perimeter, are laborious to obtain or sometimes impractical by manual methods. A simple, inexpensive and yet accurate solution to this challenging issue is the application of machine vision method coupled with image processing. This method uses a common digital document scanner for image acquisition along with ImageJ, an open source free software, to perform the dimensional measurements. ImageJ has several built-in commands that can analyze the images and produce several useful measurements that can be directly used or further analyzed for particle size distribution. This paper presents a step-by-step procedure in measuring the dimensions of granular materials using the machine vision method. Several food grains and airborne pellet dust particles were used as example materials and the no-code machine vision method was demonstrated.

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