Hyperspectral imaging sorts walnuts

APRIL 2, 2009--Automated discrimination between walnut shell and pulp has become an imperative task in the walnut postharvest processing industry in the US. During the last several years, hyperspectral fluorescence imaging has been widely used to inspect agricultural products for quality and safety due to its full of spectral information and ability of identifying different chemical components in the subject.

Lu Jiang of the Bio-imaging and Machine Vision Lab, of The Fischell Department of Bioengineering, University of Maryland, Jiang has studied the feasibility of analyzing the difference of black walnuts shell and pulp by hyperspectral fluorescence imaging. A Gaussian-kernel-based support vector machine (SVM) approach was used to classify the walnuts shell and pulp. Experiments result with an overall 90.3% recognition rate based on 6257 samples showed that hyperspectral fluorescence imaging and proposed classification method were effective in differentiation of walnuts shell and pulp.

For more information, go to: For more information, go to: http://www.bioe.umd.edu/facstaff/tao/html/walnut_inspection.html

-- Posted by Conard Holton, Vision Systems Design, www.vision-systems.com