Grape Destemming Recognition Dataset
#target detection #image classification #agricultural automation #precision agriculture #orchard management
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-14
AI Analysis & Value Prop
The current agriculture industry faces challenges of labor shortages and efficient management, especially in orchard operations where traditional manual destemming methods are inefficient and prone to errors. Existing solutions largely rely on manpower, lacking automation, leading to high production costs and low efficiency. The Grape Destemming Recognition Dataset aims to provide high-quality image data to help develop automated destemming systems based on computer vision, to improve efficiency and reduce costs. The dataset structure includes a variety of grape images, covering different growth stages and environmental conditions. Data collection uses high-resolution cameras in different orchard environments to ensure diversity coverage. Quality control includes multiple rounds of annotation and expert review to improve consistency and accuracy of annotations. All data is stored in JPG format and categorized by type and environment.
Dataset Insights
Sample Examples
a377c90c**.jpg|6960*4640|8.20 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| grape_bunch_presence | boolean | Indicates whether the image contains a grape bunch. |
| de_stemmed_percentage | float | Represents the percentage of the grape bunch that has been de-stemmed. |
| grape_color | string | Identifies the primary color of the grapes, such as red or green. |
| background_clarity | string | Describes the clarity of the image background, such as clear or blurred. |
| stem_present | boolean | Indicates whether stems are present in the image. |
| grape_count | integer | The number of grape berries in the image. |
| leaf_presence | boolean | Indicates whether grape leaves are present in the image. |
| ripeness_level | string | Describes the ripeness of the grapes, such as unripe or ripe. |
| lighting_condition | string | Describes the lighting condition at the time of capturing the image, such as natural light or indoor lighting. |
| shadow_presence | boolean | Indicates whether there are noticeable shadows in the image. |
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 grape destemming recognition dataset?
- The grape destemming recognition dataset is an object detection dataset used to identify and detect the grape destemming process, aimed at improving agricultural automation and destemming efficiency.
- In which fields can the grape destemming recognition dataset be applied?
- The grape destemming recognition dataset is mainly applied in the agricultural sector, especially in grape processing and agricultural automation.
- What are the main benefits of using the grape destemming recognition dataset?
- The main benefits of using the grape destemming recognition dataset include increased destemming efficiency, reduced labor costs, and promotion of agricultural automation technology.
- How does the grape destemming recognition dataset enhance agricultural automation?
- By providing precise destemming identification and object detection data, the grape destemming recognition dataset aids in improving the efficiency and accuracy of automated machinery.
- What is the content of the images in the grape destemming recognition dataset?
- The dataset contains images related to the grape destemming process, which are used to train models to detect and identify destemming situations.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Grape Destemming Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/358b02c57514e0225bb1e6c4d9fd0360?cate=2},
urldate={2025-09-15},
keywords={grape destemming, target detection dataset, agricultural automation, computer vision},
version={1.0}
}Using this in research? Please cite us.
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