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Determining Wheat Vitreousness Using Image Processing and a Neural Network

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

Citation:  Paper number  026089,  2002 ASAE Annual Meeting . (doi: 10.13031/2013.10559) @2002
Authors:   Ning Wang, Floyd Dowell, Naiqian Zhang
Keywords:   Grading, Inspection, Automation, Machine Vision, Color

The GrainCheck 310 is a real-time, image-based wheat quality inspection machine that can replace tedious visual inspections for purity, color, and size characteristics of grains. It also has the potential for measuring the vitreousness of durum wheat. Different neural network calibration models were developed to classify vitreous and nonvitreous kernels and evaluated using samples from GIPSA and from fields in North Dakota. Model transferability between different inspection machines was also tested.

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