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A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain(More)
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(More)
An algorithm was developed to separate pistachio nuts with closed shells from those with open shells. It was observed that upon impact on a steel plate, nuts with closed shells emit different sounds than nuts with open shells. Two feature vectors extracted from the sound signals were Mel cepstrum coefficients and eigenvalues obtained from the principle(More)
  • 2001
Transmittance spectra (500 to 950 nm) and reflectance spectra (550 to 1700 nm) were analyzed to determine if they could be used to distinguish aflatoxin contamination in single whole corn kernels. Spectra were obtained on whole corn kernels exhibiting various levels of bright greenish–yellow fluorescence. Afterwards, each kernel was analyzed for aflatoxin(More)
Shell-kernel weight ratio is the main determinate of quality and price of hazelnuts. Empty hazelnuts and nuts containing undeveloped kernels may also contain mycotoxin producing molds, which can cause cancer. A prototype system was set up to detect empty hazelnuts by dropping them onto a steel plate and processing the acoustic signal generated when kernels(More)
Hazelnuts with damaged or cracked shells are more prone to infection with aflatoxin producing molds (Aspergillus Flavus). These molds can cause cancer. In this study, we introduce a new approach that separates damaged/cracked hazelnut kernels from good ones by using time-frequency features obtained from impact acoustic signals in an offline step. The(More)
We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the learning algorithm, we achieved a false positive rate (1-specificity) of 0.2 at the true positive rate (sensitivity) of 0.8 and an area under(More)
Due to low consumer acceptance and the possibility of immature kernels, closed‐shell pistachio nuts should be separated from open‐shell nuts before reaching the consumer. A system using impact acoustics as a means of classifying closed-shell nuts from open-shell nuts has already been shown to be feasible and have better discrimination performance than a(More)
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(More)