AE—Automation and Emerging Technologies: Specialist Neural Networks for Cereal Grain Classification

Abstract

In the past few years, artificial neural networks have gained widespread acceptance for cereal grain classification and identification tasks. With the availability of different types of neural network architectures, the choice of the architecture for a particular task becomes crucial. It was hypothesized that robust specialist networks can be designed using a combination of simple networks having similar or different network architectures. To test this hypothesis, the classification accuracies of four simple network architectures (namely, back propagation network (BPN), Ward network, general regression neural network (GRNN) and probabilistic neural network (PNN)) were compared with the accuracies given by specialist networks. Each specialist network was designed using a combination of five simple networks, each specializing in classifying one grain type. The grain types used in this study were Canada Western Red Spring (CWRS) wheat, Canada Western Amber Durum (CWAD) wheat, barley, oats and rye. To evaluate the classification accuracy of the different neural network architectures, high resolution colour images of 7500 kernels (1500 kernels of each grain type) were taken for training and testing of networks. For each kernel, eight morphological features (namely, area, perimeter, length of major axis, length of minor axis, elongation, roundness, Feret diameter and compactness) and four colour features (namely, mean, median, mode and standard deviation of the grey-level values of the objects in the image) were extracted and used as input to the neural networks. Best classification accuracies (98·7, 99·3, 96·7, 98·4, and 96·9 for barley, CWRS wheat, CWAD wheat, oats and rye, respectively) were obtained using specialist probabilistic neural networks.