Crop Drought Stress Detection Dataset
#object detection #image recognition #deep learning #agricultural monitoring #precision agriculture #crop health assessment
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-10-24
AI Analysis & Value Prop
The current agricultural sector faces significant challenges due to drought impacting crop growth. Traditional monitoring methods are often inefficient and struggle to reflect the real-time crop status. Existing solutions mostly rely on manual observation, lacking efficient and accurate technological means. This dataset aims to support AI applications in identifying crop growth status under drought stress by providing images of drought-affected crops. Data is collected using drones and high-definition cameras, captured multiple times under different climate conditions and soil environments to ensure diversity and representativeness. Quality control measures include multiple rounds of annotation and expert reviews to ensure annotation consistency and accuracy. Data is stored in JPG format, organized by category and drought level.
Dataset Insights
Sample Examples
47ab5bbe**.png|1434*2000|3.25 MB
a047884c**.png|1516*2000|3.88 MB
6fd411b4**.png|3373*2000|9.58 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| crop_type | string | The specific type or variety of crop in the image. |
| drought_severity | string | Assessment of the drought stress severity shown on the crops in the image, such as mild, moderate, or severe. |
| crop_health_status | string | The health condition of the crops in the image, such as healthy, sub-healthy, or diseased. |
| leaf_color | string | Description of the color characteristics of the crop leaves, used to assess drought stress. |
| soil_condition | string | Judgment of soil moisture and nutrient status based on the surface characteristics shown in the image. |
| irrigation_signs | boolean | Whether water wetness marks caused by irrigation are visible in the image. |
| wilting_degree | string | Determination of the degree of wilting caused by water deficiency in the crops. |
| background_clutter | string | The degree of background objects and distractions 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 Crop Drought Stress Identification Dataset?
- The Crop Drought Stress Identification Dataset is an object detection dataset consisting of images used to identify the growth state of crops under drought stress.
- In which field is this dataset mainly applied?
- This dataset is mainly applied in the agricultural field to help promote the development of precision agriculture.
- What types of data does the Crop Drought Stress Identification Dataset contain?
- The dataset contains image data, focusing on object detection tasks.
- Why is the Crop Drought Stress Identification Dataset significant for agriculture?
- By identifying the drought stress state of crops, this dataset helps farmers optimize water resource management, thereby improving crop yield and quality.
- How does the Crop Drought Stress Identification Dataset help promote precision agriculture?
- Models trained with this dataset can be used to monitor crop health, aiding intelligent decision-making in precision agriculture.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Crop Drought Stress Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/bbaed8ef0f2dd3c761475fac36a73359},
urldate={2025-09-15},
keywords={crop drought detection, agricultural dataset, object detection, deep learning, image recognition},
version={1.0}
}Using this in research? Please cite us.
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