title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð±Ð¾Ð»ÐµÐ·Ð½ÐµÐ¹ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹
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: Development of an application for the recognition of plant diseases
Creators: name: Ð ÑÑÐºÐ¾Ð²Ð°, Ð¡Ð¾ÑÐ¸Ñ
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: 2024
Subjects: Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð±Ð¾Ð»ÐµÐ·Ð½ÐµÐ¹ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹
Ð²ÐµÐ±-Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ðµ
VGG19
Flask
deep learning
convolutional neural network
recognition of plant diseases
web application

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description: The aim of the work is to develop a web application for automatic recognition of plant diseases based on image analysis using deep learning methods. In the course of the work, a comprehensive analysis of the subject area was carried out, including the study of current methods and technologies in the field of plant disease recognition. An overview of existing solutions on the market was also carried out, which helped to identify the most suitable technologies and tools for development. Based on the information received, the requirements for the web application being developed, including functional and non-functional aspects, were compiled. Then the system architecture was designed, taking into account the requirements for security, performance and scalability. The VGG19 architecture was chosen as the basis for the machine learning model, which is capable of efficiently extracting high-level features from images. Trained on a publicly available dataset taken from Kaggle, the model demonstrated high accuracy. To develop the web application, HTML, CSS and JavaScript were used for the client side, and Flask for the server side. The application provides the user with a user-friendly interface for uploading images and receiving diagnostic results in real time. The integration of the trained model into a web application was also provided, which allows you to use all the advantages of machine learning for the diagnosis of plant diseases.