Çataltepe, Z.Pearson, T.Cetin, A. Enis2016-02-082016-02-082004-071098-7576http://hdl.handle.net/11693/27428Date of Conference: 25-29 July 2004Conference name: 2004 IEEE International Joint Conference on Neural NetworksWe 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 ± 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 ± 0.03.EnglishInsect detectionLearning methodsTransmittance imagesWheat kernelsCorrelation methodsCropsFeature extractionIndependent component analysisInsect controlLearning algorithmsMathematical modelsPrincipal component analysisRadial basis function networksImage processingAlgorithmsCorrelationImage analysisMathematical modelsWheatConference Paper10.1109/IJCNN.2004.1380142