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ICA Based Band Selection for Black Walnut Shell and Meat Classification in Hyperspectral Fluorescence Imagery

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

Citation:  2007 ASAE Annual Meeting  073088.(doi:10.13031/2013.22947)
Authors:   Lu Jiang, Bin Zhu, Xiuqin Rao, Gerald Berney, Yang Tao
Keywords:   Keywords: walnut shell, wallnut meat, hyperspectral fluorescence imaging, independent component analysis, band selection, k-nearest neighbors

Abstract: There are approximately over 15.4 million acres of black walnut with each acre producing about 1000 to 1700 pounds of raw nuts in the U.S. However, only about 20 million pounds of the raw black walnuts are commercially processed every year. The reason that growers are not motivated to process the nuts is that there is not enough nut processing capacity available in the U.S. In the current walnut processing plant, small shell fragments are removed manually in order to meet the required marketable quality. This visual sorting work is a very labor intensive and difficult process because shell and meat fragments can be very similar in size and color. In this research, hyperspectral fluorescence imaging has been studied to analyze the difference type of walnut shell and meat. Although the hyperspectral fluorescence imaging has been found to be efficient for differentiating walnut shell from meat, the scanning speed of hyperspectral fluorescence imaging system is not satisfactory especially for the industry requirement of real-time online inspection. Furthermore, the cost of hyperspectral imaging system is still too expensive to be acceptable by the walnut processing plants. As a result, how to select the optimum wavelength for walnut shell and meat classification and keep the same classification performance simultaneously becomes a realistic issue.

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