Academic Journal
Προβολή σε EDS

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

Λεπτομέρειες βιβλιογραφικής εγγραφής
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
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:cmedm&genre=article&issn=14248220&ISBN=&volume=26&issue=11&date=20260523&spage=&pages=&title=Sensors (Basel, Switzerland)&atitle=YOLOv9-Based%20Detection%20of%20Diseases%20in%20Poplar%20Trees%20Using%20Histogram%20Equalization%20and%20Computer%20Vision.&aulast=Makhmudov%20F&id=DOI:10.3390/s26113320
    Name: Full Text Finder (for New FTF UI) (ns324271)
    Category: fullText
    Text: Full Text Finder
    MouseOverText: Full Text Finder
Header DbId: cmedm
DbLabel: MEDLINE
An: 42280841
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Makhmudov+F%22">Makhmudov F</searchLink>; Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Zohirov+K%22">Zohirov K</searchLink>; Department of Software and Technical Support of Computer Systems, Karshi State Technical University, Karshi 180100, Uzbekistan.<br /><searchLink fieldCode="AU" term="%22Kuvandikov+J%22">Kuvandikov J</searchLink>; Department of Computer Science and Programming, Jizzakh Branch of the National University of Uzbekistan Named After Mirzo Ulugbek, Jizzakh 130100, Uzbekistan.<br /><searchLink fieldCode="AU" term="%22Temirov+Z%22">Temirov Z</searchLink>; Department of Digital Technologies, Alfraganus University, Yukori Karakamish Street 2a, Tashkent 100190, Uzbekistan.<br /><searchLink fieldCode="AU" term="%22Bobomirzayevich+AA%22">Bobomirzayevich AA</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Mukhiddinov+M%22">Mukhiddinov M</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Muraeva+K%22">Muraeva K</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Sevinov+J%22">Sevinov J</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Bolikulov+F%22">Bolikulov F</searchLink>; Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101204366%22">Sensors (Basel, Switzerland)</searchLink> [Sensors (Basel)] 2026 May 23; Vol. 26 (11). <i>Date of Electronic Publication: </i>2026 May 23.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22MDPI%22">MDPI </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101204366 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1424-8220 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2214248220%22">14248220 </searchLink><i>NLM ISO Abbreviation: </i>Sensors (Basel) <i>Subsets: </i>MEDLINE
– Name: PublisherInfo
  Label: Imprint Name(s)
  Group: PubInfo
  Data: <i>Original Publication</i>: Basel, Switzerland : MDPI, c2000-
– Name: SubjectMESH
  Label: MeSH Terms
  Group: Su
  Data: <searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Plant+Diseases%22">Plant Diseases*</searchLink>/<searchLink fieldCode="MM" term="%22Plant+Diseases+classification%22">classification</searchLink> <br /><searchLink fieldCode="MM" term="%22Detection+Algorithms%22">Detection Algorithms*</searchLink> <br /><searchLink fieldCode="MM" term="%22Populus%22">Populus*</searchLink> <br /><searchLink fieldCode="MM" term="%22Trees%22">Trees*</searchLink><br /><searchLink fieldCode="MH" term="%22Convolutional+Neural+Networks%22">Convolutional Neural Networks</searchLink> ; <searchLink fieldCode="MH" term="%22Plant+Leaves%22">Plant Leaves</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.
– Name: Ref
  Label: References
  Group: RefInfo
  Data: Bioengineering (Basel). 2025 Aug 12;12(8):. (PMID: <searchLink fieldCode="PM" term="%2240868381%22">40868381)</searchLink><br />Sensors (Basel). 2023 Feb 13;23(4):. (PMID: <searchLink fieldCode="PM" term="%2236850710%22">36850710)</searchLink><br />Appl Opt. 2016 Jan 10;55(2):400-7. (PMID: <searchLink fieldCode="PM" term="%2226835778%22">26835778)</searchLink><br />Curr Res Food Sci. 2021 Oct 16;4:724-728. (PMID: <searchLink fieldCode="PM" term="%2234712960%22">34712960)</searchLink><br />Sensors (Basel). 2024 Aug 11;24(16):. (PMID: <searchLink fieldCode="PM" term="%2239204895%22">39204895)</searchLink>
– Name: SubjectMinor
  Label: Contributed Indexing
  Group:
  Data: <i>Keywords: </i>Poplar (Populus) diseases; YOLOv9; deep learning; histogram equalization; object detection; “poplar-disease” dataset
– Name: DateEntry
  Label: Entry Date(s)
  Group: Date
  Data: <i>Date Created: </i>20260612 <i>Date Completed: </i>20260612 <i>Latest Revision: </i>20260813
– Name: DateUpdate
  Label: Update Code
  Group: Date
  Data: 20260813
– Name: PubmedCentralID
  Label: PubMed Central ID
  Group: ID
  Data: PMC13259278
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3390/s26113320
– Name: AN
  Label: PMID
  Group: ID
  Data: 42280841
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42280841
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/s26113320
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Convolutional Neural Networks
        Type: general
      – SubjectFull: Plant Leaves
        Type: general
      – SubjectFull: Image Processing, Computer-Assisted methods
        Type: general
      – SubjectFull: Plant Diseases classification
        Type: general
      – SubjectFull: Detection Algorithms
        Type: general
      – SubjectFull: Populus
        Type: general
      – SubjectFull: Trees
        Type: general
    Titles:
      – TitleFull: YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Makhmudov F
      – PersonEntity:
          Name:
            NameFull: Zohirov K
      – PersonEntity:
          Name:
            NameFull: Kuvandikov J
      – PersonEntity:
          Name:
            NameFull: Temirov Z
      – PersonEntity:
          Name:
            NameFull: Bobomirzayevich AA
      – PersonEntity:
          Name:
            NameFull: Mukhiddinov M
      – PersonEntity:
          Name:
            NameFull: Muraeva K
      – PersonEntity:
          Name:
            NameFull: Sevinov J
      – PersonEntity:
          Name:
            NameFull: Bolikulov F
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 23
              M: 05
              Text: 2026 May 23
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 1424-8220
          Numbering:
            – Type: volume
              Value: 26
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Sensors (Basel, Switzerland)
              Type: main
ResultId 1