title: Ð¡Ð¸ÑÑÐµÐ¼Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð±Ð¾Ð»ÐµÐ·Ð½ÐµÐ¹ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾ Ð¼Ð½Ð¾Ð³Ð¾Ð¼ÐµÑÐ½ÑÐ¼ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸ÑÐ¼ RGB 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: A system for recognizing plant diseases from multidimensional descriptions of RGB leaf images
Creators: name: Ð§Ð¸Ð½Ñ ÐÐ°Ð½ Ð¢Ñ Ð°Ð½Ñ
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: 2020
Subjects: Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
ÑÐµÐºÑÑÑÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
Ð¼Ð°ÑÑÐ¸ÑÐ° GLCM
Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ñ Ð°ÑÐ°Ð»Ð¸ÐºÐ°
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
Ð½ÐµÑÐµÑÐºÐ°Ñ Ð»Ð¾Ð³Ð¸ÐºÐ°
image processing
texture analysis of the image
matrix GLCM
haralikâs features
deep learning
fuzzy logic

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«Ð¡Ð¸ÑÑÐµÐ¼Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð±Ð¾Ð»ÐµÐ·Ð½ÐµÐ¹ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾ Ð¼Ð½Ð¾Ð³Ð¾Ð¼ÐµÑÐ½ÑÐ¼ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸ÑÐ¼ RGB Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð»Ð¸ÑÑÑÐµÐ²Â». ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð±Ð¾Ð»ÐµÐ·Ð½ÐµÐ¹ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾ Ð¼Ð½Ð¾Ð³Ð¾Ð¼ÐµÑÐ½ÑÐ¼ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸ÑÐ¼ RGB Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð»Ð¸ÑÑÑÐµÐ². ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ:
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description: The subject of the graduate qualification work is: "A system for recognizing plant diseases from multidimensional descriptions of RGB leaf images." This work is devoted to the development of a system for the detection and classification of plant diseases according to multidimensional descriptions of RGB leaf images. Tasks that were solved in the course of work:
1. Development of a method for normalizing leaf images.
2. Calculation of the texture features of images - Haralickâs features.
3. Development of an algorithm for classifying plant diseases based on fuzzy logic.
4. Development of an algorithm for classifying plant diseases based on neural network.
5. Software implementation of the above algorithm and method.
The object of study is a set of images of wheat leaves. In this work, we consider the essential methods for diagnosing plant diseases using images, as methods based on a neural network, and a method using texture analysis and fuzzy logic. On our data set, we performed the method of analysis of texture characteristics to select characteristics for classifying the type of disease. The classification is implemented using the fuzzy logic method and the fully connected network method, and then the results of the two methods are compared. Before calculating the texture features of the images, we used the classical image processing method using the OpenCV library to remove noise from the image â areas of unnecessary information. Image processing helps to improve the quality of input into the system, regardless of what diagnostic method is used.
As a result of the work, a system for recognizing plant diseases from leaf images was developed using two methods: the fuzzy logic method and the neural network method. To compare the result of modeling the work of two methods, it turned out that with a small data set, the fuzzy logic method achieves an accuracy of more than 95%. And the neural network method is used only with a large data set for training. We tested the developed system on a wheat data set, but we also propose to apply it to other types of plants whose diseases are displayed on their leaves, for example, corn ....