Fast insect damage detection in wheat kernels using transmittance images

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 0.12 at the true positive rate (sensitivity) of 0.8 and an area under the ROC curve (AUC) of 0.90/spl plusmn/0.02. Combining the linear model and a radial basis function network in a committee resulted in a FP rate of 0.09 at the TP Rate of 0.8 and an AUC of 0.93/spl plusmn/0.03.