Garden Flowers Michelia Recognition Image Dataset

Currently, with the acceleration of urbanization and the growth in demand for garden greening, accurately identifying the species of garden flowers has become a key issue. However, existing plant recognition systems often suffer from insufficient accuracy due to the limitations of datasets. The Garden Flowers Michelia Recognition Image Dataset aims to address technical issues related to flower species recognition while meeting the practical needs of garden management. Data collection was completed in garden environments under natural light, using high-resolution cameras to ensure image clarity. Quality control includes multiple rounds of annotation and consistency checks, performed by a team with a botanical background comprising over 50 members. Preprocessing steps include image denoising and size normalization, and data is organized in layered folders, stored in JPG format. This dataset boasts high-quality annotation and consistency, with annotation accuracy exceeding 98%. Technical innovation is reflected in the new image enhancement techniques and automated quality assessment methods. In practice, this dataset significantly enhances the accuracy of recognition systems, thereby improving garden management efficiency. Compared to similar datasets, it features more high-resolution images and extensive species coverage, with good scalability and versatility, making it suitable for various plant recognition tasks.