An automatic algorithm for detection of infestations in X-ray images of agricultural products

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 wheat kernels infested with larvae of the granary weevil, Sitophilus granarius (L.) 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 tests on X-ray images of olives infested with the Olive Fly, Bactrocera oleae (L.), 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 when training with samples that map far away from the boundary, then applying the derived decision function to all samples to be classified.