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Mobiusi/Pomegranate-Fruit-Recognition-Image-Dataset-for-Garden-Flowers

Pomegranate Fruit Recognition Image Dataset for Garden Flowers In the current agricultural sector, efficiently recognizing and managing garden plants, particularly pomegranate fruits, is a significant challenge. Conventional manual recognition and management methods are time-consuming, labor-intensive, and have low accuracy. The application of existing image recognition technologies in complex environments still faces many bottlenecks. The construction of this dataset aims to… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Pomegranate-Fruit-Recognition-Image-Dataset-for-Garden-Flowers.

sourceHugging Facecc-by-nc-sa-4.0updated 7mo agoView on Hugging Face

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Dataset Card

Pomegranate Fruit Recognition Image Dataset for Garden Flowers

In the current agricultural sector, efficiently recognizing and managing garden plants, particularly pomegranate fruits, is a significant challenge. Conventional manual recognition and management methods are time-consuming, labor-intensive, and have low accuracy. The application of existing image recognition technologies in complex environments still faces many bottlenecks. The construction of this dataset aims to solve the problem of pomegranate fruit classification in intelligent garden plant recognition, enhancing the intelligence level of garden management. Data collection utilizes high-resolution cameras to shoot pomegranate fruits of different varieties and growth states under various lighting conditions, ensuring data diversity. A professional team of agricultural experts carried out multiple rounds of annotation, proofreading, and review to establish high-quality data annotations. The annotation team has a rich agronomy background with a scale of more than 20 people. Data preprocessing uses image enhancement, denoising, and normalization techniques, and stores in JPG format, organized and classified according to tree species, fruit maturity, and other labels. The dataset achieves 99% consistency in annotation accuracy and innovatively integrates multimodal data comparison to enhance model robustness. This dataset not only improves the accuracy of fruit recognition models, effectively solving the inefficiency of intelligent garden management but also increases computational efficiency and model performance by more than 20% compared to similar datasets. The diversity and detailed annotation of the dataset provide unique advantages in the field of fruit recognition, and its methods and technologies can be extended to other fruit trees, offering high versatility and scalability.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
fruit_color	string	The appearance color of the pomegranate fruit, such as red, yellow, etc.
fruit_size	float	The size of the pomegranate fruit, usually measured by diameter or weight.
ripeness_level	string	The ripeness state of the pomegranate fruit, such as unripe, semi-ripe, ripe.
disease_presence	boolean	Indicates whether there are fruit diseases present in the image.
leaf_condition	string	The health condition of the plant leaves, such as healthy, wilted, pest-infested.
fruit_count	integer	The count of pomegranate fruits in the image.
background_type	string	The type of background in the image, such as sky, ground, other plants.
lighting_conditions	string	The lighting conditions during the capture, such as sunny, cloudy.

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. or contact us via contact@mobiusi.com