Orchid Identification Image Dataset

In modern agriculture and horticultural management, quickly and accurately identifying specific plant species like orchids is important for improving production efficiency and carrying out scientific research. However, due to the large number of plant species and morphological similarities, traditional manual identification methods are time-consuming and labor-intensive, and prone to misjudgment. Therefore, there is an urgent need for an efficient and accurate automated identification system in current technology. This dataset aims to provide high-quality image sample resources for the automated identification and classification of orchids, meeting the needs of intelligent agriculture systems and ecological research.The images in this dataset are sourced from actual gardens and flower plantations, with collection tools including high-resolution cameras and drones, collected in outdoor scenes under natural light conditions. In terms of quality control measures, we ensured the accuracy and reliability of the data annotations through multiple rounds of expert annotation and consistency checks. The annotation team consists of 20 researchers with a botanical background. Data preprocessing includes image enhancement, denoising, and normalization to improve sample quality and meet diverse algorithmic needs. The data is ultimately stored in JPEG format and organized by plant species for ease of access and use.This dataset has significant advantages in terms of data quality, with an annotation accuracy reaching a high level of 98%, and extremely high consistency after multiple rounds of review. In terms of technical innovation, augmented reality annotation methods and the latest machine learning preprocessing techniques were adopted, driving the generalization capability of the data. In practical applications, this dataset significantly improves the accuracy and response speed of orchid identification systems and is more rigorous in detail compared to other similar datasets, making it suitable for complex ecosystem analyses. Additionally, due to the diversity of collection regions and species, this dataset possesses rarity and wide applicability, and can be extended to other plant identification and ecological monitoring tasks.