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The task of features extraction occurring in creation of systems to diagnose crop diseases using leaves images has been examined in this work. The model of diagnostic feature extraction of leaves images has been proposed for the problem solution. The main concept of the proposed model includes creation of preferred feature set simplifying the construction process of considered rule in pattern recognition given in the form of images. Experimental investigations at problem solving on diagnosis of wheat diseases with the help of leaves images have been conducted for function test of the presented model. Keywords: diagnostic systems, base fragments of images, diagnostics of plant diseases, diagnostic features of leaves, preferred features, correlation of features.

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The article deals with the problem of segmentation of digital images, which is one of the main tasks in the field of digital image processing (IP) and computer vision. To solve this problem, an algorithm was proposed based on the use of a concept based on the theory of fuzzy sets. The main idea of the proposed algorithm is the formation of subsets of interconnected pixels based on the fuzzy-to-mean method. A distinctive feature of the proposed algorithm is the definition of a set of features that define areas with similar characteristics in the space of the characteristic features of the analyzed image. The proposed segmentation algorithm (SA) consists of two stages: 1) the formation of characteristic features for all channels of the base color; 2) clustering of image elements. The practical significance of the obtained results lies in the fact that the developed models of algorithms can be used in various applied problems, where the classification of objects represented as images is provided. To test the efficiency of the developed algorithm, experimental studies were carried out in solving a number of applied problems related to color image segmentation, in particular, license plate recognition problems.