title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ¸ÑÑÐµÐ¼Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ ÑÐ¸ÑÐ¾ÑÐ°Ð½Ð¸ÑÐ°ÑÐ½ÑÑ ÑÐ³ÑÐ¾Ð· Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
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 a system for detecting phytosanitary threats using neural networks
Creators: name: ÐÑÐµÐ¼ÐµÐ½ÐºÐ¾, ÐÐ°Ð½Ð¸Ð»Ð°
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: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
u-net
Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ñ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹
machine learning
image classification
convolutional neural network
plant diseases

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐµÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸
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ÑÑÐ¾Ð¶Ð°Ñ.
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ÐÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ DenseNet121 Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° Ð½Ð°Ð¸Ð»ÑÑÑÐ¸Ð¹ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ Ð½Ð°
Ð¸ÑÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð±ÑÐ»Ð° Ð´Ð¾Ð¾Ð±ÑÑÐµÐ½Ð° Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ Ñ Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸ÐµÐ¹. Ð¢Ð¾ÑÐ½Ð¾ÑÑÑ
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ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð° 98-99 Ð¿ÑÐ¾ÑÐµÐ½ÑÐ¾Ð².
description: This work is devoted to solving the problem of automation of timely detection
of diseased plants in the fields in order to preserve the crop.
In the course of this work was compared existing methods of image
classification and selected the convolutional neural network method as the most
appropriate method.
Software has been developed that allows forming training samples based on
field photos, augmenting training samples in order to avoid over-training the model,
training image classifier models, and using implemented classifier models.
A training sample consisting of 407 examples of late blight, 344 examples of
healthy Bush, 501 examples of potato mosaic and 288 examples of weeds was
prepared using the developed software with the involvement of plant protection
specialists. The selection was expanded using image generation algorithms.
The pre-trained densenet121 model showed the best result on the source data
and was further trained on the data with augmentation. The accuracy of the trained
classifier on the exam data with augmentation was 98-99 percent.