Paulownia Flower Image Recognition Dataset in Garden

In modern garden flower management, the types of paulownia are numerous and difficult to quickly distinguish through traditional means, facing problems of low identification efficiency and high labor costs. Existing solutions typically rely on manual identification, which is susceptible to subjective influence and difficult to scale. This dataset is established to enhance the automatic recognition capability of garden flower species through machine learning, thereby assisting refined management and research. The data is collected using professional camera equipment under standard lighting conditions, ensuring consistency and clarity of visual data. Quality control measures include multiple rounds of annotation, expert review, and consistency checks, with an annotation team composed of 20 individuals with a botanical background to ensure professionalism and reliability. In the data preprocessing stage, techniques such as image size normalization, color correction, and background noise reduction are involved. The data is finally compressed and stored in JPG format and organized in directories for easy retrieval.