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- W2018467515 abstract "Canadian Grain Commission has stringent regulations on the cleanliness and uniformity ofwheat grain for both domestic and export grades. Research at laboratory levels has demonstratedthat machine vision is an effective method for classification of cereal grains. Robust machine visionalgorithms have been developed and tested to extract morphological, color and textural features ofcereal grains and dockage content. The objective of this study was to assess the ability of machinevision in classifying foreign matter (barley) in wheat using a machine vision algorithm. The samplesused in this study were bulk images of Canada Western Red Spring (CWRS) wheat mixed withknown quantities of barley (0.6 to 5%). Back propagation neural network (BPNN) and statisticalclassifiers were used for classification. Results of the study indicate that classification was reducedfrom about 94% for clean wheat to about 77% for 1.2% barley admixture and then increased again to about 97% for 3% and 5% barley admixture using neural network classifiers. This reflects that themachine vision algorithm was unable to classify 1.2.% barley admixture correctly and requires somemodification before it can be used for practical purposes." @default.
- W2018467515 created "2016-06-24" @default.
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- W2018467515 date "2006-01-01" @default.
- W2018467515 modified "2023-10-14" @default.
- W2018467515 title "Assessment of Machine Vision Algorithm for Quantification of Foreign Matter in Wheat" @default.
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- W2018467515 doi "https://doi.org/10.13031/2013.20887" @default.
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