Ornamental Flower Sakura (Verbena) Recognition Image Dataset

With the acceleration of urbanization and the growing demand for horticulture, precise identification of ornamental flowers, especially sakura, has become increasingly important. Currently, many flower identifications rely on human expertise, which is prone to errors and inefficiencies. Existing recognition software often struggles to distinguish diverse plants like sakura, leading to limitations in flower management, protection, and research applications. This dataset aims to enhance recognition accuracy through high-quality sakura images, meeting the needs of horticulture and scientific research. The images in this dataset are collected using professional cameras under various lighting and background conditions to ensure diversity. Multiple rounds of manual annotation combined with professional botanist reviews ensure the accuracy and consistency of data labeling. The annotation team consists of ten professionals with backgrounds in botany and image processing. The data preprocessing uses image enhancement and compression technology, making it suitable for machine learning training and stored in a well-defined JPG file format classification. The core advantage of this ornamental flower sakura recognition image dataset is its high-quality, diverse image annotations with an annotation accuracy rate of over 95%, ensuring consistency and completeness. Innovative image preprocessing and annotation methods are employed to improve model recognition accuracy, increasing classification accuracy by about 15%. Compared to similar datasets, this dataset stands out due to its unique scarcity of flower species and rich visual backgrounds. It is widely applicable to diverse industry needs with high scalability, supporting customized labels and secondary development, suitable for floral management and research applications of different scales of enterprises.