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United States Department of Agriculture

Agricultural Research Service

Research Project: SORTING AGRICULTURAL MATERIALS FOR DEFECTS USING IMAGING AND PHYSICAL METHODS

Location: Foodborne Toxin Detection and Prevention

Title: An Automatic Algorithm for Detection of Inclusions in X-ray Images of Agricultural Products

Authors
item Haff, Ronald
item Pearson, Thomas

Submitted to: Electronic Publication
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 26, 2007
Publication Date: July 18, 2007
Citation: Haff, R.P., Pearson, T.C. 2007. An Automatic Algorithm for detection of Infestations in X-ray Images of Agricultural Products. Sensing and Instrumentation for Food Quality and Safety. 1(3):143-150

Interpretive Summary: A computer program was developed and tested for detection of certain defects or contaminants in x-ray images of food products The program was tested on x-ray images of wheat kernels infested with larvae of the granary weevil and the results were compared to those obtained by human subjects evaluating digitized x-ray film images (14.4% overall error vs. 15.6% for human subjects). The program was also tested on x-ray images of olives infested with the Olive Fly, yielding a total error of 12% for large infestations and over 50% for the smallest infestations with false positive (good product classified as bad) results below 10%. Certain training strategies for the computer program were derived and tested.

Technical Abstract: An automatic recognition algorithm was developed and tested for detection of certain defects or contaminants in x-ray images of agricultural commodities. Testing of the algorithm on x-ray images of wheat kernels infested with larvae of the granary weevil yielded comparable results to those obtained by human subjects evaluating digitized x-ray film images (14.4% overall error vs. 15.6% for human subjects). Further testing on x-ray images of olives infested with the Olive Fly yielded a total error of 12% for large infestations and over 50% for the smallest infestations with false positive results below 10%. Testing of alternate training strategies showed that for this type of algorithm, which uses a form of discriminant analysis with a generally “fuzzy” decision boundary, best results are obtained training with samples that map far away from the boundary, then applying the derived decision function to all samples to be classified.

Last Modified: 8/27/2014
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