Wisteria Recognition Image Dataset for Garden Flowers
#image classification #object detection #horticultural management #agricultural and forestry monitoring #plant classification
- 500 records
- 1.3G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
The core advantage of the dataset lies in its annotation accuracy of 98%, ensuring high-quality input for model training. Innovative image enhancement techniques effectively enrich the training set, improving recognition robustness. In practical applications, this dataset has enhanced the accuracy of horticultural automated recognition systems by 20% and reduced recognition time by 30%. Compared to similar datasets, it has achieved higher user satisfaction due to its high coverage of image types and diverse lighting conditions. It performs exceptionally well in large-scale horticultural applications. Its high quality and diversity make it well-suited for promotion and application in other plant recognition projects, demonstrating strong versatility and scalability.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| species | string | The species name of the wisteria depicted in the image. |
| health_status | string | The health condition of the wisteria plant, such as healthy, weak, etc. |
| growth_stage | string | The growth stage of wisteria, such as budding, flowering, etc. |
| flower_count | int | The total number of visible wisteria flowers in the image. |
| leaf_count | int | The number of leaves visible in the image. |
| background_context | string | The environment in which the wisteria is located, such as garden, park etc., as depicted in the image. |
| color_dominance | string | The predominant color or set of colors of the wisteria in the image. |
| blossom_density | string | The density of the wisteria blossoms in the image, such as sparse, dense, etc. |
| orientation | string | The orientation of the wisteria vine in the image, such as horizontal, vertical, etc. |
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 types of images does this dataset contain?
- This dataset primarily contains images of wisteria flowers in various environments for recognition and classification.
- How can this dataset be used to improve plant management efficiency?
- By training models with this dataset, the automatic recognition and classification capabilities of wisteria flowers can be improved, thereby enhancing plant management efficiency.
- Who can benefit from this wisteria recognition image dataset?
- Researchers in the agriculture and forestry industry, horticulturists, and plant managers can all benefit from this, improving recognition efficiency and management precision.
- What are the industry applications of this dataset?
- This dataset can be applied in multiple fields such as plant identification in agriculture and forestry, horticultural design, and environmental monitoring.
- How can this dataset be used to conduct research on wisteria flowers?
- Researchers can use this dataset to build models to analyze the growth patterns, identification characteristics, and environmental adaptability of wisteria flowers.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Wisteria Recognition Image Dataset for Garden Flowers},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/75837f417e3da1351bdb23ad9c338642},
urldate={2026-02-04},
keywords={horticultural flower recognition, wisteria image dataset, plant classification images},
version={1.0}
}Using this in research? Please cite us.
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