Garden Flower Peony (Herbaceous) Recognition Image Dataset

In modern agriculture and horticulture management, plant species are numerous and varied, and traditional manual recognition methods are both time-consuming and prone to errors. Especially when planting and managing flowers like peonies, promptly and accurately identifying varieties is a major challenge. Currently, many automatic recognition solutions lack sufficient sample data to support the accuracy and stability of algorithms. This dataset aims to solve the problem of automatic recognition of garden flowers peony, improve the accuracy of image recognition models, and support wider applications through a rich collection of image samples.The dataset captures images of peony plants from multiple angles and at different growth stages under natural light conditions using high-resolution cameras, ensuring coverage of their seasonal changes. Data quality is ensured through multiple rounds of annotation and consistency checks, reviewed by a professional team in the field of botany, with a team size of over 20 people. Data preprocessing includes image correction, background removal, and standardization, finally being stored in JPG format and systematically organized through labeling.The dataset features high annotation precision and stability, with annotation consistency of over 95% for all images, and maintains image integrity under various weather and lighting conditions. Through innovative multi-task learning annotation methods and data augmentation techniques, the adaptability of recognition algorithms has been significantly enhanced, showing a 15% increase in accuracy in comparative studies. This dataset supports the actual needs of intelligent forestry and agriculture recognition, significantly optimizing planting management activities, and, as a comprehensive solution, demonstrates higher scalability and versatility than similar datasets. It especially provides scarce data resources for less-studied plants like peonies.