Recognition of Sun-pest Infected Wheat Kernels Using Artificial Neural Networks

In this study it is aimed to recognize sun-pest infected kernels in a sample sub-group of wheat kernels taken from a bulk of Bezostaja wheat. Recognition of the damaged kernels is realized by evaluating light transmittance data of the kernels through use of artificial neural networks (ANN). Wheat kernels in the sub-group are left to fall in an oblique groove with semi-circular cross-section. While the kernels cross a LED light source, light transmitted through the kernel fall on a sensor just across the light source. Analog signals induced by the sensor are recorded and histograms of these signals are evaluated by using ANN in order to recognize sun-pest infected wheat kernels in the sub-group. Two different ANN models: multi layer perceptron (MLP) and self organizing map (SOM) models were used in the recognition process.