Garden Flowers Orychophragmus violaceus Recognition Image Dataset

Currently, the horticultural field faces challenges in diversity and complexity for plant recognition, especially in accurately identifying specific varieties such as Orychophragmus violaceus. Existing solutions mainly rely on manual experience, which is inefficient and not highly accurate. This dataset aims to improve the accuracy and efficiency of Orychophragmus violaceus recognition through machine learning methods to meet the automation needs of horticultural management. The dataset was collected from multiple horticultural scenes using high-resolution cameras under different lighting and weather conditions. It underwent multiple rounds of rigorous annotation and consistency checks, reviewed and corrected by a team of horticultural experts with a total team size of 20 people. Data preprocessing includes image denoising, standardization, and brightness adjustment. Data is stored in JPG format, organized by plant type and collection environment. This dataset features high annotation accuracy (over 95%), data consistency, and coverage of various growth environments. Compared to similar datasets, it uses advanced image enhancement techniques to improve model generalization capabilities. This dataset solves the error problem of traditional manual recognition and can significantly enhance the level of horticultural management automation. Additionally, it is characterized by a rich diversity of scenes and rare plant posture images, making it easily expandable to recognition tasks of other plants. Compared to other resources, it not only has significant advantages in data quality but also demonstrates high versatility and technological advancement in its application domain.