Datasets:
tags:
- image segmentation
- deep learning training
- crop monitoring
- precision agriculture
- weed management
license: cc-by-nc-sa-4.0
task_categories:
- image-segmentation
language:
- en
pretty_name: Field Crop and Weed Segmentation Dataset
size_categories:
- 1B<n<10B
Field Crop and Weed Segmentation Dataset
The current agricultural industry faces challenges in managing crops and weeds. Traditional manual identification is inefficient and prone to errors, leading to a decrease in crop yield. Existing solutions rely heavily on manual intervention, lacking efficient automated tools to meet the fine management needs of modern agriculture. This dataset aims to support the training of deep learning models by providing high-quality crop and weed semantic segmentation data, thereby improving the automation level of crop identification and weed monitoring. Data collection uses high-resolution cameras to capture field images under various conditions, ensuring diversity and representativeness. For quality control, we implemented multiple rounds of annotation and consistency checks and invited agricultural experts for review to ensure annotation accuracy and consistency. Data will be stored in JPG format for images and JSON format for label information, facilitating subsequent analysis and use.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| crop_type | string | The type of crop present in the image, such as rice, wheat, etc. |
| weed_presence | boolean | Indicates whether weeds are present in the image. |
| lighting_condition | string | The lighting conditions at the time the image was taken, such as sunny or cloudy. |
| growth_stage | string | The growth stage of the crop, such as seeding or maturity. |
| soil_type | string | The type of soil visible in the image, such as clay or sandy. |
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