Dataset Viewer
Auto-converted to Parquet Duplicate
file_name
stringclasses
5 values
quality
stringclasses
5 values
crop_type
stringclasses
5 values
damage_type
stringclasses
4 values
leaf_color
stringclasses
3 values
leaf_shape
stringclasses
4 values
12ed4d69dfeb815a599c0e0eeae2ff70.png
1535*2000
Unknown
Insect damage
Green
Elliptical
1737a7ca6dfc7458c98cb4e8b8682e32.png
2679*2000
Unable to determine
Insect damage
Yellow-green
Oval
51748cc0133481646d09f50779b06d2b.png
2752*2000
Undetermined
Pest damage
Yellow
Oval
6ef7b94d0a4ccfab856442584c413da8.png
1475*2000
Cucumber
Disease
Green
Heart-shaped
f9056d850d720f31e7f9c857efa98581.png
2683*2000
Unknown Crop
Pest Damage
Green
Irregular Edges

Crop Leaf Damage Detection Dataset

The agriculture sector currently faces challenges in crop yield and quality due to diseases and pests, especially with the intensification of climate change. Farmers require effective monitoring tools. Existing monitoring solutions largely rely on manual inspections, which are time-consuming and prone to errors. This dataset aims to provide high-quality images of leaf damage to help AI models better identify and monitor crop health. Data collection is conducted using professional cameras in a well-lit field environment, ensuring image clarity. We implement multiple rounds of labeling and expert review to ensure consistency and accuracy in labeling. The data is stored in JPG format and organized by damage type for easy processing and analysis. The dataset features high labeling precision, with damage type consistency reaching over 90%, and is well-complete. By introducing new data augmentation techniques, model recognition accuracy improved by 15%. This dataset not only addresses the real-time needs of agricultural monitoring but also enhances the disease and pest resistance of crops, aiding the development of smart agriculture.

Technical Specifications

Field Type Description
file_name string File name
quality string Resolution
crop_type string The type of crop to which the leaf belongs, such as rice, wheat, etc.
damage_type string The type of damage on the leaf, such as pest, disease, physical damage, etc.
leaf_color string The color of the leaf, which may reflect its health status, such as green, yellow, brown, etc.
leaf_shape string The shape features of the leaf, such as elliptical, heart-shaped, needle-shaped, etc.

Compliance Statement

Authorization Type CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial Use Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization No PII, no real company names, simulated scenarios follow industry standards
Compliance System Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com

Downloads last month
11