Medal Chrysanthemum Recognition Image Dataset for Garden Flowers

Currently, the recognition and classification of garden flowers are crucial for landscape design and ecological protection. However, many traditional manual classification methods are inefficient and prone to errors. Existing image recognition algorithms have limitations in recognizing species in complex environments and diverse species. This dataset aims to enhance the accuracy and efficiency of automatic recognition of medal chrysanthemum varieties through high-quality annotated images. The dataset includes images of medal chrysanthemums collected under different lighting conditions using high-definition digital cameras. Multiple rounds of expert annotation and consistency checks ensure the annotation accuracy of the data. The annotation team consists of experts with a background in botany, numbering up to 20 people. Data preprocessing includes steps such as image quality enhancement and standardized size, stored in JPG format for fast access and processing. The annotation accuracy of this dataset is as high as 95%, with strict guarantees of consistency and integrity. The use of innovative multimodal annotation methods enhances the recognition performance of rare samples. Clustered image classification increases recognition speed by 50%. Compared with similar datasets, it has higher species coverage and adaptability to changing natural scenes. Its core advantage lies in the high quality and uniqueness of the data, making it suitable for various plant recognition tasks with broad application potential.