A comprehensive image dataset of jute diseases
Md Masudul Islam1, Md Ripon Sheikh1
1Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Data in Brief
|December 24, 2025
Summary
A new Jute Diseases Image Dataset aids machine learning for plant disease diagnosis. This resource supports early detection of jute (Corchorus) diseases, crucial for agriculture.
Area of Science:
- Agricultural Science
- Computer Vision
- Plant Pathology
Background:
- Accurate diagnosis of jute (Corchorus) diseases is vital for crop yield.
- Limited availability of labeled jute disease image data hinders machine learning model development.
Purpose of the Study:
- To present a comprehensive Jute Diseases Image Dataset for machine learning applications.
- To facilitate the development of automated systems for jute plant disease identification.
Main Methods:
- Collected and curated 1390 high-resolution images of jute plant leaves.
- Categorized images into five classes: Dieback, Holed, Mosaic, Stem Soft Rot, and Fresh (healthy).
- Applied pre-processing including resizing (1024x1024) and duplicate removal; created augmented train-test split versions.
Main Results:
- The dataset includes 1390 images across five distinct jute disease categories.
- Images captured under varied conditions enhance model generalizability.
- Visualization techniques (Grad-CAM) validated model attention on disease-relevant areas.
Conclusions:
- The Jute Diseases Image Dataset addresses a critical data gap for agricultural AI.
- Enables timely disease management and supports jute cultivation, especially in key regions like Bangladesh.
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