Harvest Worker Pose Estimation Dataset
#Pose estimation #object detection #behavior recognition #Crop harvesting #agricultural production monitoring #human-machine collaboration
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-13
AI Analysis & Value Prop
The agricultural sector is increasingly facing serious issues of labor shortages and low production efficiency, particularly in the harvesting stage where traditional manual methods are inefficient and labor-intensive. Existing automation solutions often lack precise recognition of worker movements, making effective human-machine collaboration challenging. Therefore, this dataset aims to solve pose recognition and behavior analysis problems of workers during the harvesting process by providing high-quality pose estimation data, thereby enhancing the intelligence level of agricultural production. The dataset includes 5,000 images of harvest workers at work, annotated with each worker's key pose points and target bounding boxes to ensure data accuracy and completeness. Data collection was carried out with high-resolution cameras in actual farmland environments, with strict controls on lighting and angles to improve data quality. A multi-round annotation and expert review mechanism was adopted to ensure consistency and accuracy of the annotations. Data is stored in JPG format, organized by date and worker ID for easy retrieval and use.
Dataset Insights
Sample Examples
9730309d**.jpg|2964*3705|1.05 MB
20761080**.jpg|5184*3456|2.27 MB
8fdc6cee**.jpg|6000*4000|3.92 MB
b07a0abc**.jpg|3963*5945|4.04 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| worker_pose | string | Describes the specific posture of workers in the image, such as standing, bending, etc. |
| worker_count | integer | The total number of workers appearing in the image. |
| object_presence | string | Whether there are objects such as fruit or tools in the image along with the workers. |
| environment_type | string | The type of environment where the workers are located, such as orchard or greenhouse. |
| action_type | string | The specific action being performed by the worker, such as picking or carrying. |
| clothing_type | string | The type of clothing worn by the workers, which may assist in analyses. |
| weather_condition | string | The weather condition shown in the image, such as sunny or cloudy. |
| light_condition | string | Description of the light intensity in the image, such as bright or dim. |
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 |
Frequently Asked Questions
- What is the Harvest Worker Pose Estimation Dataset?
- The Harvest Worker Pose Estimation Dataset is a collection focused on analyzing and estimating the poses of workers in the agricultural field to support smart agriculture development.
- What are the application scenarios of the Harvest Worker Pose Estimation Dataset?
- This dataset is mainly used in worker pose monitoring within agricultural automation systems and the development of intelligent care robots to enhance agricultural productivity and safety.
- Why is the object detection dataset important for agricultural intelligence?
- Object detection datasets are crucial for agricultural intelligence as they aid in developing accurate automation tools to recognize and evaluate worker poses, increasing operational efficiency and reducing labor costs.
- How does this dataset assist in farm management?
- By analyzing the poses of harvest workers, farm management can optimize labor distribution, enhance picking efficiency, and ensure worker safety, thereby increasing overall agricultural output.
- How to use this dataset for machine learning training?
- Researchers can use this dataset to train machine learning models for automated pose detection and evaluation, thus developing smarter agricultural machines and systems.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Harvest Worker Pose Estimation Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/4475dfee9b4a05fe732ce6c9859219e0},
urldate={2025-10-22},
keywords={pose estimation dataset, agricultural dataset, object detection, harvest worker pose},
version={1.0}
}Using this in research? Please cite us.
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