Garden Flowers Plumbago Recognition Image Dataset

Currently, the garden management industry faces challenges such as the vast variety of plant species, difficulty in identification, and high labor costs. Existing plant identification solutions often rely on human input, which is susceptible to human error, limiting both accuracy and efficiency. As a common garden plant, the identification and management of Plumbago are of great significance to garden beautification. This dataset aims to provide high-quality images of Plumbago to help address technical issues and business needs related to automated precise identification and management.The data is captured using professional camera equipment under natural light, collected in different seasons and environments to ensure diversity and authenticity. After collection, images undergo multiple rounds of expert annotation and consistency checks to ensure annotation accuracy. The data annotation team consists of over 20 PhDs and masters in botany and horticulture. Data preprocessing includes color correction, image denoising, and cropping standardization, stored in JPG format by time and location.The core strength of this dataset is its annotation accuracy exceeding 95%, ensuring consistency and completeness in recognition. Innovative multi-angle shooting and machine learning-assisted annotation techniques effectively enhance data authenticity and diversity. The dataset significantly improves the automation efficiency of garden plant identification, increasing accuracy by more than 20%. Compared to other plant recognition datasets, the Plumbago Recognition Image Dataset is more focused on specific species and provides rare multi-environment, multi-season samples. Additionally, the dataset has excellent expandability and versatility, applicable to recognition tasks for other similar plants, leading by 20% over similar datasets.