Yorulmaz, OnurPearson, T.C.Çetin, A. Enis2016-02-082016-02-0820110277-786Xhttp://hdl.handle.net/11693/28354Date of Conference: Proceedings of SPIE, Sensing for Agriculture and Food Quality and Safety IIIDate of Conference: 26–27 April 2011In this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).EnglishCepstrum AnalysisFungus Detection in Popcorn KernelsImage ProcessingSVMCepstral featuresCepstrumCepstrum analysisFeature extraction methodsKernel imageRecognition ratesSVMAgricultureFeature extractionFood safetyImaging systemsSupport vector machinesImage processingConference Paper10.1117/12.882406