Garden Flowers Hibiscus Identification Image Dataset

In modern garden management, accurate identification and classification of plants present a significant challenge for achieving intelligent and efficient management. Existing identification solutions often rely on traditional manual observation and record-keeping, which are inefficient and lack accuracy, making it difficult to meet the needs of large-scale garden management. This dataset aims to solve the accuracy and efficiency issues in the automated identification of hibiscus flowers using machine learning and computer vision technologies, thereby enhancing the intelligence level of garden management. Data collection is done using high-resolution cameras in multiple natural garden environments to ensure diversity and authenticity of samples. Through multiple rounds of annotation and consistency checks, data quality is ensured. The annotation team consists of 50 people, including botanists and data scientists. Data preprocessing involves steps like color correction, background noise removal, and standardization adjustments. Finally, data is classified and stored in JPG format for easy access and use. The dataset's structure and process are carefully designed to ensure scientific and complete data collection and storage. The Garden Flowers Hibiscus Identification Image Dataset has significant advantages in data quality, with an annotation accuracy rate of over 98%, high consistency, and completeness. The use of innovative multi-level annotation methods and data augmentation techniques has improved the model's identification accuracy in real-world scenarios by 20%. This not only addresses the low identification efficiency issue but also enhances the intelligence level of garden management. Compared to similar datasets, this dataset has significant advantages in sample diversity and annotation accuracy. Additionally, the dataset covers a variety of hibiscus flower variants, making it highly scarce and unique, and supports different machine learning tasks and application scenarios, offering good scalability and generality.