Mobiusi/Crop-Waterlogging-Condition-Detection-Dataset

Crop Waterlogging Condition Detection Dataset

The current agricultural sector faces challenges related to climate change and water resource management. The issue of crop waterlogging is becoming increasingly serious, affecting crop growth and yield. Existing monitoring methods largely rely on manual inspection, which is inefficient and prone to errors, unable to provide real-time feedback on crop status. This dataset aims to help AI systems quickly identify and predict crop waterlogging and hypoxia status through image data. Data is collected using drones and ground camera equipment in various fields and environmental conditions to ensure coverage of different growth stages and waterlogging scenarios. Regarding quality control, multiple rounds of annotation and expert review are conducted to ensure label consistency and accuracy. Data is stored in JPG format, organized by image ID, facilitating subsequent analysis and use. The core advantage of this dataset is its high annotation precision and consistency, with annotation accuracy exceeding 95%, significantly improving monitoring efficiency. Newly introduced image enhancement technology increases the model's robustness, allowing accurate waterlogging condition identification under various environmental conditions, helping farmers take timely actions to improve crop yield and address issues in practical production.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
crop_type	string	Identify the type of crop appearing in the image, such as rice, wheat, etc.
water_level	string	Identify the level of water flooding the crops, such as no flooding, mild flooding, moderate flooding, or severe flooding.
crop_health	string	Evaluate the impact of flooding on crop health, such as healthy, damaged, or severely damaged.
growth_stage	string	Identify the growth stage of crops, such as seedling stage, growth stage, or mature stage.
soil_condition	string	Assess the moisture level or other visual characteristics of the soil.
image_quality	string	Evaluate the clarity and quality of the image, such as clear, blurred, or too noisy.
light_condition	string	Identify the lighting condition when the image was taken, such as bright or dim.
weather_condition	string	Confirm the weather condition when the image was taken, such as sunny, cloudy, or rainy.

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