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README.md

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+---
+tags:
+- image classification
+- plant disease detection
+- intelligent planting management
+- garden flower management
+- agricultural technology applications
+- plant recognition systems
+license: cc-by-nc-sa-4.0
+task_categories:
+- image-classification
+language:
+- en
+pretty_name: Pansy Recognition Image Dataset
+size_categories:
+- 1B<n<10B
+---
+
+# Pansy Recognition Image Dataset
+
+Currently, garden management faces the challenge of efficiently and accurately identifying flower varieties. Traditional manual identification relies on experience and is inefficient. Existing image recognition technologies still need improvement in the accuracy of specific flower types, especially in complex backgrounds. This dataset aims to address common accuracy deficiencies in pansy recognition by providing a large number of high-quality images to meet the needs of intelligent recognition system development. Data is collected using professional photographic equipment under natural light and various backgrounds to ensure diversity and authenticity. Quality control includes multiple rounds of expert labeling, a combination of machine preliminary classification, and manual review to ensure high labeling precision. The team consists of botany experts and data labeling professionals, with a scale of more than 10 members. In data preprocessing, the latest image enhancement technology is used to improve model training effectiveness. The storage format is JPG, and data is organized by flower category and shooting conditions. The dataset features high precision labeling and consistency in data quality, with a labeling accuracy rate of over 98%. Innovative semi-supervised learning labeling methods are adopted to enhance the dataset&#039;s scalability. Compared to similar datasets, this dataset demonstrates stronger application value under diverse and natural collection conditions, particularly in improving the accuracy of flower recognition. It offers greater background diversity and distribution rarity compared to other datasets, suitable for secondary development and application promotion of various intelligent applications, supporting cross-scenario expanded applications.
+
+## Technical Specifications
+
+| Field | Type | Description |
+| :---  | :---  | :--- |
+| file_name | string | File name |
+| quality | string | Resolution |
+| flower_species | string | The specific type or subspecies of the pansy. |
+| color_pattern | string | The combination and pattern of colors on the pansy petals. |
+| bloom_status | string | The blooming status of the pansy at the time of the photo, e.g., budding, full bloom. |
+| plant_health | string | The observed health condition of the pansy plant (healthy, diseased, etc.). |
+| leaf_characteristics | string | The morphology and color characteristics of the pansy plant leaves. |
+| flower_count | integer | The number of pansy flowers in the image. |
+| background_objects | string | Description of the objects in the background surrounding the pansy in the image. |
+| light_conditions | string | The lighting conditions during the image capture, such as natural light or shadow. |
+| image_quality | string | The clarity and color reproduction quality of the image. |
+
+## Compliance Statement
+
+<table>
+  <tr>
+    <td>Authorization Type</td>
+    <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td>
+  </tr>
+  <tr>
+    <td>Commercial Use</td>
+    <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td>
+  </tr>
+  <tr>
+    <td>Privacy and Anonymization</td>
+    <td>No PII, no real company names, simulated scenarios follow industry standards</td>
+  </tr>
+  <tr>
+    <td>Compliance System</td>
+    <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td>
+  </tr>
+</table>
+
+## Source & Contact
+
+If you need more dataset details, please visit [Mobiusi](https://www.mobiusi.com/datasets/0325fad4c44e34847040c84985660ed1?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com