Garden Flower Conical Hydrangea Image Recognition Dataset

With the rapid development of the landscaping industry, garden plants, especially flowers, have a wide variety, making accurate identification and classification a major challenge for the industry. Existing manual identification and traditional image recognition methods have shortcomings such as being time-consuming and having low accuracy. The construction of this dataset aims to enhance the accuracy and efficiency of robotic systems in automatically recognizing garden flowers, addressing key technical issues in automated flower recognition. Data collection was conducted using professional HD cameras under various weather and lighting conditions to ensure sample diversity. The data underwent rigorous multi-round manual annotation and consistency checks, reviewed by botanical experts to ensure high-quality annotation. The annotation team consists of five botanical experts and ten image processing professionals, making it a large-scale operation. Data preprocessing includes image enhancement, noise reduction, and white balance adjustment, and is finally stored and organized in standard JPG format for easy retrieval and access.