Garden Flower Paphiopedilum (Slipper Orchid) Recognition Image Dataset

Currently, the recognition and management of garden flowers are significant challenges in agriculture, forestry, and fisheries. Traditional recognition methods suffer from low accuracy and high labor costs, making large-scale and refined flower management difficult. Especially for Paphiopedilum (Slipper Orchid), due to its complex morphology and variable colors, higher recognition precision is required. This dataset provides high-quality images of Paphiopedilum to address the industry's needs for fine-grained flower classification and intelligent recognition, aiding in improving horticultural management efficiency. Data is captured in natural light using high-resolution digital cameras to ensure the capture of true details. Quality control includes multiple rounds of annotation by professional horticultural engineers and consistency checks to ensure the accuracy of the labels. The annotation team is composed of 15 experts with a horticultural background. Data preprocessing includes image denoising, color correction, and size normalization, and it is organized in standard JPG format for direct use in deep learning models.