Mobiusi/Orchid-Identification-Image-Dataset

Orchid Identification Image Dataset

In modern agriculture and horticultural management, quickly and accurately identifying specific plant species like orchids is important for improving production efficiency and carrying out scientific research. However, due to the large number of plant species and morphological similarities, traditional manual identification methods are time-consuming and labor-intensive, and prone to misjudgment. Therefore, there is an urgent need for an efficient and accurate automated identification system in current technology. This dataset aims to provide high-quality image sample resources for the automated identification and classification of orchids, meeting the needs of intelligent agriculture systems and ecological research.The images in this dataset are sourced from actual gardens and flower plantations, with collection tools including high-resolution cameras and drones, collected in outdoor scenes under natural light conditions. In terms of quality control measures, we ensured the accuracy and reliability of the data annotations through multiple rounds of expert annotation and consistency checks. The annotation team consists of 20 researchers with a botanical background. Data preprocessing includes image enhancement, denoising, and normalization to improve sample quality and meet diverse algorithmic needs. The data is ultimately stored in JPEG format and organized by plant species for ease of access and use.This dataset has significant advantages in terms of data quality, with an annotation accuracy reaching a high level of 98%, and extremely high consistency after multiple rounds of review. In terms of technical innovation, augmented reality annotation methods and the latest machine learning preprocessing techniques were adopted, driving the generalization capability of the data. In practical applications, this dataset significantly improves the accuracy and response speed of orchid identification systems and is more rigorous in detail compared to other similar datasets, making it suitable for complex ecosystem analyses. Additionally, due to the diversity of collection regions and species, this dataset possesses rarity and wide applicability, and can be extended to other plant identification and ecological monitoring tasks.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
flower_species	string	The specific flower species to which the Dendrobium belongs.
plant_stage	string	The current growth stage of the Dendrobium, such as seedling, flowering, and fruiting.
flower_color	string	The color of the Dendrobium flowers.
leaf_shape	string	The leaf shape characteristics of the Dendrobium.
blossom_count	int	The number of Dendrobium blossoms in the image.
stem_height	float	The height of the Dendrobium stem in centimeters.
leaf_color	string	The color of the Dendrobium leaves.
environment_condition	string	The light, humidity, and other conditions of the environment where the Dendrobium is located.
disease_symptom	string	Possible disease symptoms present on the Dendrobium as observed in the image.
background_type	string	The type of background in the image, such as natural, horticultural facilities, 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

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com