| Issue |
BIO Web Conf.
Volume 160, 2025
IV International Conference on Improving Energy Efficiency, Environmental Safety and Sustainable Development in Agriculture (EESTE2024)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 8 | |
| Section | Sustainable Development in Agriculture | |
| DOI | https://doi.org/10.1051/bioconf/202516001004 | |
| Published online | 12 February 2025 | |
Diagnostics of plant diseases based on symptoms from leaf images
1 Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent, Uzbekistan
2 Tashkent Institute of Management and Economics, Tashkent, Uzbekistan
3 Sejong University, South Korea, Seoul, Korea
* Corresponding author: dilnoz134@rambler.ru
The paper develops algorithms for diagnosing plant diseases based on symptoms from leaf images. Various features, such as changes in color, shape, and texture of leaves, associated with diseases are analyzed. It investigates how symptoms affect the accuracy of disease recognition using machine learning. It is revealed that the use of visual symptoms and other symptoms significantly improves diagnostic results. Not only that but it is determined that the combination of deep learning and classical image processing methods allows for high accuracy in disease detection. Furthermore, it is established that the developed algorithms can identify plant diseases based on symptoms detected in leaf images. The results of testing the algorithms on real data show their applicability in the real world.
© The Authors, published by EDP Sciences, 2025
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