Application of image analysis for the varietal classification of barley:: Morphological features

This paper presents an exploratory investigation of the application of morphological features in image analysis for varietal classification of Polish spring barley. The objective of this study was to determine the utility of morphological features for classifying individual kernels of five varieties of barley. Furthermore, this study was performed to find the best method to classify kernels of barley with the lowest error of classification. Image processing consisted of several steps: image acquisition, segmentation, external and internal image feature extraction, classification and interpretation. Each barley kernel was described using 74 morphological features. The selection was carried out using three methods based on: Fisher's coefficient, probability of error and average correlation coefficient and mutual information. Principle component analysis (PCA), linear discriminant analysis (LDA), and non-linear discriminant analysis (NDA) were used throughout this paper as the classification methods. The results confirmed that the method using morphological features may be successfully employed in image analysis for preliminary varietal identification of barley kernels. Furthermore, LDA was found to be the method which best separated different varieties of objects.