Cepstrum based feature extraction method for fungus detection

Onur Yorulmaz; Tom C. Pearson; A. Enis Çetin

doi:10.1117/12.882406

2 June 2011 Cepstrum based feature extraction method for fungus detection

Onur Yorulmaz, Tom C. Pearson, A. Enis Çetin

Proceedings Volume 8027, Sensing for Agriculture and Food Quality and Safety III; 80270E (2011) https://doi.org/10.1117/12.882406
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

In 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%.

Citation Download Citation

Onur Yorulmaz, Tom C. Pearson, and A. Enis Çetin "Cepstrum based feature extraction method for fungus detection", Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270E (2 June 2011); https://doi.org/10.1117/12.882406

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