Datasets:
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
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