title: ÐÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð°Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ° Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾ ÑÐµÐºÑÑÑÑÐ½ÑÐ¼ Ñ Ð°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ°Ð¼ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð»Ð¸ÑÑÑÐµÐ²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Computer diagnostics of plant diseases according to textural characteristics of leaf images
Creators: name: ÐÐµÐ»ÑÑÐ¸Ð½, ÐÐ¼Ð¸ÑÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2019
Subjects: Ð½Ð¾ÑÐ¼Ð°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Ð¼Ð°ÑÑÐ¸ÑÐ° GLCM
Ð¼Ð°ÐºÑÐ¸Ð¼ÑÐ¼ ÑÑÐ½ÐºÑÐ¸Ð¸ Ð¿ÑÐ¸Ð½Ð°Ð´Ð»ÐµÐ¶Ð½Ð¾ÑÑÐ¸
Ð½ÐµÑÐµÑÐºÐ°Ñ Ð»Ð¾Ð³Ð¸ÐºÐ°
Ð±Ð¸Ð½Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ñ
Ð¼Ð°Ð¶Ð¾ÑÐ¸ÑÐ°ÑÐ½Ð¾Ðµ Ð³Ð¾Ð»Ð¾ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
image normalization
GLCM matrix
membership function maximum
fuzzy logic
binarization
majority voting

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description: The object of study is a dataset of 1000 images of wheat leaves. The main goal of the work was the development and implementation of software for obtaining reliable recognition of the 10 main types of wheat diseases in the images. Existing methods are explored and a digital image processing method is proposed for diagnosing plant diseases using digital RGB photographic images of leaves in visible spectrum using the GLCM matrix, comparing the results of mathematical expectations for the maximum functions inherent in subsequent voting recognition. A mathematical modeling of the proposed element has been passed successful and a classifier has been developed and implemented to determine the type of wheat disease. The newest work is that computer diagnostics allows to detect the symptoms of plants disease when photographing leaves by means of GLCM matrices and interpreting the results using fuzzy logic. The proposed approach when grouped by 25 images allows to obtain reliable recognition in more than 90% of cases. In the future, the developed software has all the chances of being introduced into autonomous UAVs for automatic recognition of areas damaged by pathologies, sown with various cultivated plants. Successful integration of the developed software into agriculture will reduce the physical burden of farmers, will allow time to diagnose emerging foci of diseases of cultivated plants, after the elimination of which the yield will increase.