The Cyprus Seasonal Flora Image Dataset (CSFID)

The dataset comprises a collection of 3,072 labelled training and 768 test images of 27 plant species commonly found in Cyprus. Each image represents a distinct flora class captured under real-world conditions, including herbs, shrubs, trees, and flowering plants. The images vary in lighting, background, and scale, making this dataset suitable for robust training and evaluation of multi-class plant classification models in machine learning and computer vision tasks. It is designed to facilitate the training of convolutional neural networks (CNNs) and transfer learning architectures by offering naturally captured, high-variance imagery that reflects realistic environmental conditions such as varying lighting, occlusion, and background clutter. This dataset serves as a valuable resource for benchmarking plant classification algorithms and advancing the field of AI-assisted botanical studies.本数据集收录了塞浦路斯常见27种植物的3072张标注训练图像与768张测试图像。每张图像对应一个独立的植物类别，采集自真实自然环境，涵盖草本植物、灌木、乔木与显花植物。图像在光照、背景与拍摄尺度上存在多样变化，适用于机器学习与计算机视觉任务中的多分类植物分类模型的稳健训练与评估。 本数据集通过提供自然采集的高方差图像——这类图像可反映光照变化、遮挡与背景杂乱等真实环境条件——旨在助力卷积神经网络（Convolutional Neural Networks, CNNs）与迁移学习架构的训练。该数据集可作为植物分类算法基准测试的宝贵资源，同时推动AI辅助植物学研究领域的发展。