title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾ ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸ÑÐ¼ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ 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 mobile application for the diagnosis of plant diseases from photographs based on neural networks
Creators: name: ÐÑÐ³Ð»Ð°ÐµÐ², Ð¡ÐµÐ¼ÐµÐ½
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: Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ñ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð´Ð°Ð½Ð½ÑÑ
YOLO
plant diseases
deep learning
object detection
data processing

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ° Ð´Ð»Ñ ÑÐ²Ð¾ÐµÐ²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð±Ð¾Ð»ÐµÐ·Ð½ÐµÐ¹ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾ ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸ÑÐ¼ Ð¸Ñ Ð»Ð¸ÑÑÑÐµÐ². Ð Ð¿ÑÐ¾ÑÐµÑÑÐµ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´ÐµÑÐµÐºÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐµÐ³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð²ÑÐ±ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ YOLOv8. ÐÐ°ÑÐ°ÑÐµÑ ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¹ PlantDoc Ð±ÑÐ» Ð²ÑÐ±ÑÐ°Ð½ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸, Ð±ÑÐ»Ð° ÑÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð° Ð¾Ð±ÑÑÐ°ÑÑÐ°Ñ Ð²ÑÐ±Ð¾ÑÐºÐ°, Ð¸Ð· ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ Ð±ÑÐ»Ð¸ Ð¸ÑÐºÐ»ÑÑÐµÐ½Ñ ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¸ Ð½ÐµÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÑÑÑÐ¸Ðµ ÑÐµÐ°Ð»ÑÐ½ÑÐ¼Ð¸ Ð´Ð°Ð½Ð½ÑÐ¼ Ñ Ð¼ÐµÑÑ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ ÑÐµÐ»ÑÑÐºÐ¾ÑÐ¾Ð·ÑÐ¹ÑÑÐ²ÐµÐ½Ð½ÑÑ ÑÐ°Ð±Ð¾Ñ. ÐÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ YOLOv8 nano Ð±ÑÐ»Ð° Ð±ÑÐ»Ð° Ð¾Ð±ÑÑÐµÐ½Ð° Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ Ñ Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸ÐµÐ¹. ÐÐ»Ñ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð° Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²Ð»ÐµÐ½Ð° Ð¾Ð±ÑÑÐ°ÑÑÐ°Ñ Ð²ÑÐ±Ð¾ÑÐºÐ°, ÑÐ¾ÑÑÐ¾ÑÑÐ°Ñ Ð¸Ð· 883 Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹. ÐÐ»Ñ ÑÐ°Ð·Ð¼ÐµÑÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð±ÑÐ» Ð¿ÑÐ¸Ð²Ð»ÐµÑÐµÐ½ ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸ÑÑ Ð¸Ð· Ð¾Ð±Ð»Ð°ÑÑÐ¸. Ð¢Ð¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ (precision) Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½Ð° ÑÐµÑÑÐ¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð° 85 Ð¿ÑÐ¾ÑÐµÐ½ÑÐ¾Ð². ÐÐ¾Ð»Ð½Ð¾ÑÐ° Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ (recall) â 49 Ð¿ÑÐ¾ÑÐµÐ½ÑÐ¾Ð². Ð¡ÑÐµÐ´Ð½ÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ (mAP) - 71 Ð¿ÑÐ¾ÑÐµÐ½Ñ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð±ÑÐ»Ð° Ð¸Ð½ÑÐµÐ³ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð° Ð² ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ðµ Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð¾Ðµ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ðµ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾Ðµ ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð±ÐµÐ· Ð´Ð¾ÑÑÑÐ¿Ð° Ð² Ð¸Ð½ÑÐµÑÐ½ÐµÑ, Ð´ÐµÐ»Ð°ÑÑ ÑÐ½Ð¸Ð¼ÐºÐ¸ Ñ ÐºÐ°Ð¼ÐµÑÑ Ð¸Ð»Ð¸ Ð·Ð°Ð³ÑÑÐ¶Ð°ÑÑ ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¸ Ñ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð° Ð¸ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÑ Ð´ÐµÑÐµÐºÑÐ¸Ñ Ð±Ð¾Ð»ÐµÐ·Ð½ÐµÐ¹ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¸.
description: This work is devoted to the development of a tool for the timely recognition of plant diseases from photographs of their leaves. In the course of the work, a comparison of existing methods for detecting objects in images was performed. An algorithm based on the architecture of the YOLOv8 neural network model was chosen as the most appropriate method. The PlantDoc photo dataset was selected for training. After preprocessing, a training sample was formed and photos that did not correspond to real data from the sites of agricultural work were excluded. The pre-trained YOLOv8 nano model was trained on augmentation data. For testing, a training sample was prepared, consisting of 883 images of plants downloaded from the Internet, using automated tools. A specialist from the field was brought in to mark up the data. The accuracy of prediction (precision) of the trained model based on test data is 85 percent. The completeness of predictions (recall) is 49 percent. The mean average accuracy of predictions (mAP) is 71 percent. The resulting model was integrated into a developed mobile application capable of working without Internet access, taking pictures from the camera or uploading photos from the device and detecting diseases in the image.