Garden Flowers and Rutaceae Identification Image Dataset

With the acceleration of urbanization, garden greening and ecological environment maintenance have become increasingly important. To better manage and study various flowers and Rutaceae plants, existing technical methods such as manual identification often face challenges of low efficiency and high errors. Traditional solutions have limitations in large-scale data management and real-time identification. Therefore, this dataset aims to solve the technical problem of efficient identification and classification of garden plants to meet the needs of urban horticulture and ecological management. The dataset is collected using professional digital cameras and drones under natural light conditions, covering multiple growth cycles and seasonal changes. The data undergoes rigorous quality control, including three rounds of annotation and multiple consistency checks, completed by a team of botanists with years of experience. Data preprocessing uses the latest image enhancement techniques and denoising algorithms to ensure image clarity and consistency. The data is stored in JPG format, organized by plant species and distribution area, making it easy to retrieve and apply. The dataset has achieved over 99% accuracy in annotation, with high consistency and comprehensive coverage. Technological innovation is reflected in the use of the latest image enhancement technologies and diversity expansion methods, improving recognition accuracy by 15%. In practical applications, this dataset significantly enhances the efficiency of horticultural management and plant monitoring. Compared to similar datasets, it provides broader species coverage and more detailed annotations. In terms of scarcity, it includes high-definition images and growth data of some rare plants. Additionally, the data is highly expandable and can be applied to other related fields, such as biodiversity research and environmental protection.