Abstract: Seed size is an important grading factor in pulse grains. Seed processors andcanning industry prefer shipments with minimal size variability to those with a wider range ofseed distribution. Typically, seed size distribution of a batch of grains is determined by sieving arepresentative sample from a batch. Image analysis based seed sizing provides a faster, moreconsistent, accurate and effortless alternative to sieving. The objective of this study was todevelop a machine vision system for sizing seeds of various shapes. A flatbed scanner basedimage analysis application was developed to size circular (peas), elliptical (soybean) andmultifaceted (chickpeas) shaped seeds by imaging a bulk poured sample. This applicationautomatically separates the seed boundaries in an image, measures individual seeds, andreports size distribution for user-selectable sieve combination in metric or imperial units.Development of the image analysis system along with its performance in comparison withmanual sieving is discussed in this paper.
Publication Year: 2004
Publication Date: 2004-01-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 10
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