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Peer-reviewedCC BYSensors

YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision

  • Fazliddin Makhmudov1
  • Kudratjon Zohirov2
  • Jura Kuvandikov3
  • Zavqiddin Temirov4
  • Abdusalomov Akmalbek Bobomirzayevich Boymatov Elbek Otabek o'g'li5,6,7,8
  • Mukhriddin Mukhiddinov6,9,8
  • K. (Kh) Muraeva6,8
  • Jasur Usmonovich Sevinov10,7
  • Furkat Bolikulov1
10 affiliations
  1. 1Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea
  2. 2Department of Software and Technical Support of Computer Systems, Karshi State Technical University, Karshi 180100, Uzbekistan
  3. 3Department of Computer Science and Programming, Jizzakh Branch of the National University of Uzbekistan Named After Mirzo Ulugbek, Jizzakh 130100, Uzbekistan
  4. 4Department of Digital Technologies, Alfraganus University, Yukori Karakamish Street 2a, Tashkent 100190, Uzbekistan
  5. 5Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
  6. 6Department of Artificial Intelligence, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan
  7. 7Department of Information Processing and Control Systems, Tashkent State Technical University, Tashkent 100095, Uzbekistan
  8. 8Department of Robotics and Intelligent Systems, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan
  9. 9Department of Industrial Management and Digital Technologies, Nordic International University, Tashkent 100128, Uzbekistan
  10. 10Department of Computer Engineering, University of Tashkent for Applied Sciences, Tashkent 100149, Uzbekistan
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{makhmudov2026yolov9based,
  title = {{YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision}},
  author = {Fazliddin Makhmudov and Kudratjon Zohirov and Jura Kuvandikov and Zavqiddin Temirov and Abdusalomov Akmalbek Bobomirzayevich Boymatov Elbek Otabek o'g'li and Mukhriddin Mukhiddinov and K. (Kh) Muraeva and Jasur Usmonovich Sevinov and Furkat Bolikulov},
  journal = {Sensors},
  year = {2026},
  month = may,
  doi = {10.3390/s26113320},
  url = {https://doi.org/10.3390/s26113320},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision
AU  - Fazliddin Makhmudov
AU  - Kudratjon Zohirov
AU  - Jura Kuvandikov
AU  - Zavqiddin Temirov
AU  - Abdusalomov Akmalbek Bobomirzayevich Boymatov Elbek Otabek o'g'li
AU  - Mukhriddin Mukhiddinov
AU  - K. (Kh) Muraeva
AU  - Jasur Usmonovich Sevinov
AU  - Furkat Bolikulov
PY  - 2026
DA  - 2026/05/23
JO  - Sensors
DO  - 10.3390/s26113320
UR  - https://doi.org/10.3390/s26113320
AB  - 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.
SN  - 1424-8220
ER  -