| Issue |
E3S Web Conf.
Volume 386, 2023
Annual International Scientific Conferences: GIS in Central Asia – GISCA 2022 and Geoinformatics – GI 2022 “Designing the Geospatial Ecosystem”
|
|
|---|---|---|
| Article Number | 01007 | |
| Number of page(s) | 8 | |
| Section | GIS in Agriculture | |
| DOI | https://doi.org/10.1051/e3sconf/202338601007 | |
| Published online | 12 May 2023 | |
Smart system for early detection of agricultural plant diseases in the vegetation period
“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Kary Niyaziy Str., 39, 100000, Tashkent, Uzbekistan
* Corresponding author: rbaratov@mail.ru
This paper presents a smart system for the early detection of agricultural plant diseases in the vegetation period. The proposed smart system allows the detection of three types of wheat diseases, particularly yellow rust, powdery mildew, and Septoria at an early stage, and significantly improves the soil and ecology by locally spraying harmful chemicals just on sick plants. The proposed disease-detecting method is based on the structure of a convolutional neural network (CNN) using the Pycharm program based on the C ++ programming language. The basic structure of the smart system consists of Raspberry PI 4 MODULE, Raspberry Pi camera module v2, buzzer, HC-SR04 distance sensor, rotor driver, AC motor, power supply, relay microelectronics, and some digital devices.
Key words: plant disease / wheat / flour dew / yellow spot / Septoria / image / smart system / sensor / neural networks / algorithm / microelectronics / digital device / block diagram
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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