Soil Moisture Estimation Dataset
#object detection #image classification #soil monitoring #agricultural decision-making #precision agriculture
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-05-07
AI Analysis & Value Prop
The current agricultural sector faces the problem of insufficient soil moisture monitoring. Traditional methods often rely on manual detection, which is inefficient and lacks accuracy. Existing solutions mainly depend on simple sensor measurements, lacking in-depth analysis of regional soil moisture changes. Therefore, this dataset aims to provide image data based on surface cracking, thus promoting AI model training and soil moisture level estimation. Data collection is conducted using high-resolution cameras to capture surface cracking in different farmland environments. To ensure data quality, multiple rounds of labeling and expert reviews are implemented to ensure data consistency and accuracy. Data is stored in JPEG format and organized structurally by time and location.
Dataset Insights
Sample Examples
4d8b9639**.png|1434*2000|3.25 MB
97e53bc3**.png|1516*2000|3.88 MB
9017d20b**.png|3373*2000|9.58 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| soil_crack_density | float | Indicates the density of soil cracks per unit area. |
| crack_depth_average | float | Refers to the average depth of the surface soil cracks. |
| crack_width_average | float | Measures the average width of soil cracks. |
| soil_color | string | The surface color that may reflect soil moisture. |
| vegetation_coverage | float | The proportion of vegetation covering the soil surface. |
| reflectance_index | float | Calculated by analyzing image light reflectance properties. |
| object_count | integer | The number of detected objects in the image. |
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 is the purpose of the Soil Moisture Estimation Dataset?
- This dataset is used for estimating soil moisture levels, aiding in better water resource management in agriculture.
- How does this dataset assist in agricultural management?
- By analyzing images of surface cracking, agricultural managers can more accurately assess soil moisture, optimizing irrigation strategies.
- Why is soil moisture estimation important for agriculture?
- Soil moisture affects plant growth and yield, and accurate estimation can enhance agricultural efficiency and resource utilization.
- What type of imagery data does this dataset use?
- The dataset is based on images showing the degree of surface cracking to aid in estimating soil moisture.
- Are there specific algorithms suitable for analyzing this dataset?
- Object detection algorithms like YOLO or Faster R-CNN are suitable for analyzing this imagery data.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Soil Moisture Estimation Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/c8f9c7f30f39d47752f9049ff1de86ea?cate=2},
urldate={2025-09-15},
keywords={soil moisture, object detection, agricultural dataset, image data, AI model},
version={1.0}
}Using this in research? Please cite us.
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