Cauliflower Diseases Identification Image Dataset

The cauliflower disease image dataset consists of four well-defined classes: Bacterial Spot Rot, Black Rot, Downy Mildew, and Healthy Fruit. Each class includes 100 original RGB images with a resolution of 224×224 pixels, captured under natural lighting to preserve real-world features. To improve model performance and increase data diversity, 600 augmented images per class were generated using standard augmentation techniques such as rotation, flipping, zooming, brightness variation, noise addition, and translation. This brings the total number of images in the dataset to 2,800, with 700 images for each class. The dataset is well-balanced and specifically curated to support deep learning-based classification and detection of common cauliflower diseases, providing a reliable benchmark for training and evaluation of plant disease recognition models.本花椰菜病害图像数据集包含四个定义明确的类别：细菌性斑点腐病（Bacterial Spot Rot）、黑腐病（Black Rot）、霜霉病（Downy Mildew）以及健康花椰菜果实（Healthy Fruit）。每个类别包含100张原始RGB图像，分辨率为224×224像素，均在自然光照条件下拍摄以保留真实场景特征。为提升模型性能并扩充数据多样性，我们采用旋转、翻转、缩放、亮度调节、添加噪声、平移等标准数据增强技术（standard augmentation techniques），为每个类别生成600张增强图像。至此，该数据集总图像量达2800张，每个类别均包含700张图像。该数据集类别分布均衡，专为支持常见花椰菜病害的基于深度学习（deep learning）的分类与检测任务而构建，可为植物病害识别模型的训练与评估提供可靠的基准测试集。