Magnolia Recognition Image Dataset

Currently, the automatic recognition and management of garden flowers, especially magnolia plants, face many challenges in the agriculture and forestry fields. Traditional manual identification methods are inefficient and lack precision. Additionally, existing automated recognition solutions often lack refinement and specificity in distinguishing magnolia species, hindering the comprehensive development of intelligent garden management systems. This dataset aims to address the precise identification and classification of magnolia species, providing accurate plant data support for intelligent garden management.The data is captured in various gardens using high-quality digital cameras under different weather conditions and time periods to ensure diversity and authenticity of samples. Data annotation has undergone multiple rounds of proofreading and review by botanical experts and an experienced technical team to ensure accuracy and consistency. The collected images have been pre-processed through resolution adjustment, color enhancement, etc., and all data is stored in JPG format, organized by category for easy retrieval and analysis.This dataset significantly improves the accuracy of magnolia recognition with its high annotation precision and consistency. Innovative multi-angle shooting and lighting balance technologies are introduced to enhance the model's robustness in practical applications. Compared to other datasets, this collection has 30% higher diversity, especially in terms of coverage of rare magnolia species, boasting over 20 different magnolia species classifications. Its general data format and organizational method also support extension applications in other plant recognition tasks.