Grape Destemming Task Status Classification Dataset
#image classification #status recognition #machine learning #agricultural production #task monitoring #smart agriculture
- Records
- 20000 records
- Size
- 3.1G
- Format
- JPG/PNG/JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-04
AI Analysis & Value Prop
The current agricultural industry faces challenges of labor shortages and low production efficiency. Traditional manual monitoring of task status is not only time-consuming but also prone to errors. Existing solutions largely rely on manual inspection, lacking automated detection methods, leading to delayed task progress updates. To improve operation efficiency and accuracy, this dataset aims to provide high-quality image data support for the automated monitoring of grape destemming task status. The data is collected using high-resolution cameras under natural light conditions, covering different environments and task statuses. To ensure data quality, we implemented multiple rounds of annotation and consistency checks, and all annotations were reviewed by agricultural experts. The data is stored in JPEG format and organized by task status classification. Through this approach, we ensure high data quality and usability.
Overview
Dataset Insights
Preview
Sample Examples
2403852d**.jpg|6960*4640|8.20 MB
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| grape_ripeness | string | Indicates the ripeness state of grapes in the image, such as unripe, ripe, overripe, etc. |
| stem_removal_progress | string | Marks the progress of stem removal process, such as not started, in progress, completed. |
| worker_presence | boolean | Indicates whether there is a worker present in the image engaged in stem removal operations. |
| equipment_in_use | string | Identifies the type of stem removal equipment used in the image, such as mechanical destemmer, manual tools. |
| lighting_condition | string | Describes the lighting conditions in the image, such as natural light, artificial light, dim lighting, etc. |
Trust
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
FAQ
Frequently Asked Questions
- What is the Grape Destemming Operation Status Classification Dataset?
- The Grape Destemming Operation Status Classification Dataset is an image classification dataset designed for the analysis of grape destemming operations in the agricultural field.
- What agricultural applications is this dataset suitable for?
- This dataset is suitable for grape destemming operation monitoring, smart agriculture management, and production efficiency optimization.
- What are the benefits of using this dataset?
- Using this dataset can increase the level of automation in grape destemming processes, reduce labor costs, and improve operational efficiency.
- What is the main objective of the dataset?
- The main objective of the dataset is to identify and classify different statuses of grape destemming operations through image classification techniques to better manage these agricultural processes.
- How can this dataset be used for machine learning research?
- Researchers can use this dataset to train and test image classification models, thereby advancing machine learning applications in the agricultural sector.
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Cite this Work
@dataset{Mobiusi2025,
title={Grape Destemming Task Status Classification Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/cccb2d6a281d55fdf82287a22e53662f?cate=2},
urldate={2025-09-15},
keywords={grape destemming, agricultural image classification, task status monitoring, smart agriculture dataset},
version={1.0}
}Using this in research? Please cite us.
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