Machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution

Abstract

Texture analysis and classification of image components belong to common problems of the interdisciplinary area of digital signal and image processing. The paper is devoted to the pattern matrix construction using features evaluated by the discrete Fourier transform (DFT) or the discrete wavelet transform (DWT) using the relative power in selected frequency bands or scale levels, respectively. Image features are then used to recognize groups of similar pattern vectors by self-organizing neural networks forming a mathematical tool for cluster analysis. Further classification methods including the decision tree, support vector machine, nearest neighbour method and neural networks are then applied for construction of specific models and evaluation of their accuracy and cross validation errors. The proposed algorithm is applied for analysis of given microscopic images representing wax structures covering breathing openings on leaves affected by environmental pollution in different locations. The classification accuracy depends upon the method used and it is higher than 92% for all experiments.