Academic Journal
YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision.
| Τίτλος: | YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision. |
|---|---|
| Συγγραφείς: | Makhmudov F; Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea., Zohirov K; Department of Software and Technical Support of Computer Systems, Karshi State Technical University, Karshi 180100, Uzbekistan., Kuvandikov J; Department of Computer Science and Programming, Jizzakh Branch of the National University of Uzbekistan Named After Mirzo Ulugbek, Jizzakh 130100, Uzbekistan., Temirov Z; Department of Digital Technologies, Alfraganus University, Yukori Karakamish Street 2a, Tashkent 100190, Uzbekistan., Bobomirzayevich AA; Department of Artificial Intelligence, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan.; Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan.; Department of Information Processing and Control Systems, Tashkent State Technical University, Tashkent 100095, Uzbekistan.; Department of Robotics and Intelligent Systems, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan., Mukhiddinov M; Department of Artificial Intelligence, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan.; Department of Robotics and Intelligent Systems, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan.; Department of Industrial Management and Digital Technologies, Nordic International University, Tashkent 100128, Uzbekistan., Muraeva K; Department of Artificial Intelligence, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan.; Department of Robotics and Intelligent Systems, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan., Sevinov J; Department of Information Processing and Control Systems, Tashkent State Technical University, Tashkent 100095, Uzbekistan.; Department of Computer Engineering, University of Tashkent for Applied Sciences, Tashkent 100149, Uzbekistan., Bolikulov F; Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea. |
| Πηγή: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 May 23; Vol. 26 (11). Date of Electronic Publication: 2026 May 23. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI, c2000- |
| Ιατρικοί όροι (MeSH): | Image Processing, Computer-Assisted*/methods , Plant Diseases*/classification , Detection Algorithms* , Populus* , Trees*, Convolutional Neural Networks ; Plant Leaves |
| Περίληψη: | Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., "Parsha (Scab)," "Brown spotting," "White-Gray spotting," and "Rust," which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management. |
| References: | Bioengineering (Basel). 2025 Aug 12;12(8):. (PMID: 40868381) Sensors (Basel). 2023 Feb 13;23(4):. (PMID: 36850710) Appl Opt. 2016 Jan 10;55(2):400-7. (PMID: 26835778) Curr Res Food Sci. 2021 Oct 16;4:724-728. (PMID: 34712960) Sensors (Basel). 2024 Aug 11;24(16):. (PMID: 39204895) |
| Contributed Indexing: | Keywords: Poplar (Populus) diseases; YOLOv9; deep learning; histogram equalization; object detection; “poplar-disease” dataset |
| Entry Date(s): | Date Created: 20260612 Date Completed: 20260612 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC13259278 |
| DOI: | 10.3390/s26113320 |
| PMID: | 42280841 |
| Βάση Δεδομένων: | MEDLINE |
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