Texture analysis and artificial neural networks for identification of cereals—case study: wheat, barley and rape seeds

Abstract

AbstractThe scope of the research comprises an analysis and evaluation of samples of rape, barley and wheat seeds. The experiments were carried out using the author’s original research object. The air flow velocities to transport seeds, were set at 15, 20 and 25 m s−1. A database consisting of images was created, which allowed to determine 3 classes of kernels on the basis of 6 research variants, including their transportation way via pipe and the speed of sowing. The process of creating neural models was based on multilayer perceptron networks (MLPN) in Statistica (machine learning). It should be added that the use of MLPN also allowed identification of rape seeds, wheat seeds and barley seeds transported via pipe II at 20 m s−1, for which the lowest RMS was 0.05 and the coefficient of classification accuracy was 0.94.