Crop Waterlogging Condition Detection Dataset
#Object Detection #Condition Recognition #Crop Monitoring #Agricultural Management #Environmental Monitoring
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-05-10
AI Analysis & Value Prop
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.
Dataset Insights
Sample Examples
74f05744**.png|1499*2000|3.85 MB
9a07e330**.png|3045*2000|6.87 MB
df71640d**.png|1688*2000|3.98 MB
a6edfb62**.png|1604*2000|2.94 MB
80cf85b8**.png|2092*2000|3.96 MB
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 | Proprietary - Commercial AI Training License (No Redistribution) |
| 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 |
Frequently Asked Questions
- What can this dataset be used for?
- The crop flooding state detection dataset can be used for researching and developing intelligent algorithms to identify the growth conditions of crops under flooding conditions, aiding in agricultural management optimization.
- How many images are included in the dataset?
- The dataset contains multiple images, and the exact number may be specified within the dataset details.
- How does this dataset help in agriculture?
- By detecting and analyzing crop flooding conditions, agricultural managers can better respond to flooding events, improve crop productivity, and mitigate potential disaster impacts.
- How does this dataset enhance the intelligent management of crops under flooding conditions?
- The information provided by the dataset can be used to train machine learning models that automatically identify and respond to flooding threats, thus enabling more intelligent crop management.
- What technical skills are required to use this dataset?
- Users need to have fundamental knowledge of image processing, machine learning, and deep learning, especially in training and applying object detection models.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Crop Waterlogging Condition Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/ec271bf3a4be30ddafc48ff7283cfa3c},
urldate={2025-09-15},
keywords={Crop Monitoring, Waterlogging Detection, Agricultural Dataset, Object Detection},
version={1.0}
}Using this in research? Please cite us.
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