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.本数据集包含3072张带标注的训练图像与768张测试图像，涵盖塞浦路斯常见的27种植物。每张图像对应一个独立的植物类群，拍摄于真实自然环境中，包含草本植物、灌木、乔木与开花植物。图像在光照、背景与拍摄尺度上存在多样差异，可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可靠训练与评估。 本数据集旨在通过提供自然采集、高方差的图像，助力卷积神经网络（Convolutional Neural Networks, CNNs）与迁移学习架构的训练，这些图像可反映真实环境中的光照变化、遮挡以及背景杂乱等情况。本数据集可作为植物分类算法基准测试与推动AI辅助植物学研究领域发展的宝贵资源。