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Related Experiment Videos

SAR-MLD1-2025-MangoLeaf: A comprehensive high-quality mango leaf dataset for disease classification.

Md Robiul Awoal1,2, Afjal H Sarower1,2, Md Zubair Islam1,2

  • 1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

Data in Brief
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

A new dataset of 4921 mango leaf images from Bangladesh aids in early disease detection. This resource supports developing machine learning models for identifying diseases like Anthracnose and Powdery Mildew, crucial for crop yield.

Keywords:
Computer visionDisease classificationMachine learningMango leaf

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Botany

Background:

  • Mango production in Bangladesh is significantly impacted by leaf diseases, affecting yield and quality.
  • Early detection of mango leaf diseases is critical for effective management and preventing widespread crop damage.
  • Automated disease detection systems can enhance agricultural practices and improve disease management strategies.

Purpose of the Study:

  • To present a comprehensive dataset of mango leaf images for research purposes.
  • To facilitate the development of machine learning models for automated mango leaf disease classification.
  • To provide a valuable resource for botanical and agricultural research in Bangladesh.

Main Methods:

  • Collected 4921 raw image samples of mango leaves from Naogaon District, Bangladesh.
  • Categorized images into five classes: Healthy, Anthracnose, Powdery Mildew, Turning Brown, and Gall Midge.
  • Captured images under natural lighting conditions to ensure authenticity and variability.

Main Results:

  • A curated dataset of 4921 labeled mango leaf images is now available.
  • The dataset includes diverse visual representations of healthy and diseased mango leaves.
  • The images captured under natural light preserve intrinsic visual features for robust analysis.

Conclusions:

  • The presented dataset is a significant resource for advancing research in automated mango leaf disease detection.
  • This dataset will aid researchers in developing and validating machine learning models for improved disease diagnosis.
  • Utilizing this dataset can lead to more effective disease management strategies, benefiting mango cultivation in Bangladesh and beyond.