Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley

Image analysis using neural modeling is one of the most dynamically developing methods employing artificial intelligence. The feature that caused such widespread use of this technique is mostly the ability of automatic generalization of scientific knowledge as well as the possibility of parallel analysis of the empirical data. A properly conducted learning process of artificial neural network (ANN) allows the classification of new, unknown data, which helps to increase the efficiency of the generated models in practice. Neural image analysis is a method that allows extracting information carried in the form of digital images. The paper focuses on the determination of imperfections such as contaminations and damages in the malting barley grains on the basis of information encoded in the graphic form represented by the digital photographs of kernels. This choice was dictated by the current state of knowledge regarding the classification of contamination that uses undesirable features of kernels to exclude them from use in the malting industry. Currently, a qualitative assessment of kernels is carried by malthouse-certified employees acting as experts. Contaminants are separated from a sample of malting barley manually, and the percentages of previously defined groups of contaminations are calculated. The analysis of the problem indicates a lack of effective methods of identifying the quality of barley kernels, such as the use of information technology. There are new possibilities of using modern methods of artificial intelligence (such as neural image analysis) for the determination of impurities in malting barley. However, there is the problem of effective compression of graphic data to a form acceptable for ANN simulators. The aim of the work is to develop an effective procedure of graphical data compression supporting the qualitative assessment of malting barley with the use of modern information technologies. Image analysis can be implemented into dedicated software.