Beehive Environment Monitoring Dataset
#Object Detection #Image Classification #Beehive Monitoring #Crop Management #Smart Agriculture
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-05-02
AI Analysis & Value Prop
The current agricultural field faces challenges such as low monitoring efficiency and insufficient data. Traditional monitoring methods rely on manual observation and are difficult to obtain real-time data. Existing solutions focus on small-scale monitoring, which is insufficient to meet the large-scale agricultural environmental needs. This dataset aims to help agricultural researchers and enterprises improve monitoring efficiency and meet the needs of smart agriculture by providing high-quality beehive environment monitoring images. Data collection is conducted using drones and high-definition cameras in various environments to ensure diversity and representativeness. For quality control, a combination of multi-round annotation and expert review is used to ensure the accuracy and consistency of annotations. Data is stored in JPG format, organized in chronological and locational order for easy subsequent analysis and processing.
Dataset Insights
Sample Examples
3dac3d9f**.jpg|6960*4640|5.81 MB
3a07847a**.jpg|6686*4457|5.71 MB
49abff67**.jpg|5472*3648|4.47 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| hive_count | int | The number of beehives detected in the image. |
| bee_presence | boolean | Indicates whether bees are present in the image. |
| flower_count | int | The number of flowers detected in the image. |
| weather_condition | string | The weather condition shown in the image. |
| vegetation_density | float | Analysis of vegetation density in the image. |
| hive_orientation | string | The orientation of the beehives in the image. |
| light_intensity | float | The intensity of light detected in the image. |
| image_clarity | float | Score of the clarity of 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 type of dataset is the Beehive Environment Monitoring Dataset?
- The Beehive Environment Monitoring Dataset is an object detection dataset primarily used for detecting and monitoring targets within the beehive environment.
- Who would benefit from the Beehive Environment Monitoring Dataset?
- Agricultural researchers, beekeepers, and environmental monitoring professionals might benefit from this dataset as it can be used to improve beehive management and environmental analysis.
- What applications can the Beehive Environment Monitoring Dataset be used for?
- The dataset can be used for monitoring environments within beehives, target recognition, and training image processing algorithms to support agriculture-related smart applications.
- What are the characteristics of the images in the Beehive Environment Monitoring Dataset?
- The images in the dataset are high-quality, suitable for precise object detection tasks, and captured under various environmental conditions to reflect the real beehive environment.
- What technologies can the Beehive Environment Monitoring Dataset enhance?
- This dataset can enhance the performance of object detection algorithms, particularly machine learning models applied in agriculture and environmental monitoring.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Beehive Environment Monitoring Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/40336054075318cd2fde4c8345e57645},
urldate={2025-09-15},
keywords={Beehive Monitoring Dataset, Agriculture Dataset, Object Detection, Smart Agriculture, Environment Monitoring},
version={1.0}
}Using this in research? Please cite us.
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