Mobiusi/Field-Crop-and-Weed-Segmentation-Dataset

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