An Annotated Image Dataset for Cucumber Leaf Disease Detection and Segmentation in Complex Orchard Environments

This dataset was developed to support automated detection and localization of cucumber leaf abnormalities under complex orchard conditions. The research hypothesis is that visually observable disease damage and abnormal leaf coloration can be identified from field images using deep-learning-based classification and object-detection models, despite variations in illumination, viewing angle, scale, background clutter, leaf overlap, and growth stage. The dataset contains 500 cucumber leaf images: 250 images of healthy leaves and 250 images showing disease symptoms or abnormal coloration. Images were acquired in real cultivation environments and subsequently inspected and organized according to leaf condition. The annotated subset is accompanied by text label files containing rectangular bounding-box annotations. Each annotation row has five values: the class identifier, the horizontal and vertical coordinates of the bounding-box center, and the box width and height. Class 0 represents abnormal color—that is, a visible leaf color other than healthy green, such as yellowing or browning—whereas class 1 represents visible disease-related damage. An image may contain multiple annotations because several affected regions can occur on the same leaf. The dataset includes 2,705 annotated regions: 1,741 color regions and 964 damage regions. The annotation distribution indicates that abnormal coloration occurs more frequently than directly identifiable damage regions. This reflects the heterogeneous visual presentation of cucumber leaf disorders and makes the dataset useful for studying both general discoloration and localized physical symptoms. The healthy images can support image-level binary classification, while the annotated images can be used for object detection and rectangular region localization. The current dataset version provides bounding boxes rather than pixel-level segmentation masks; therefore, it should not be interpreted as conventional semantic- or instance-segmentation ground truth. Researchers may use the dataset to train, validate, and compare deep-learning architectures for healthy-versus-affected leaf classification, color-abnormality detection, disease-damage localization, transfer learning, data augmentation, and robustness evaluation under natural cultivation conditions. Before model development, users should verify image–label correspondence, preserve the class mapping, and create non-overlapping training, validation, and test partitions. Because the annotations describe visible phenotypic categories rather than pathogen-specific diagnoses, predictions should be interpreted as indications of abnormal coloration or damage and not as confirmation of a particular cucumber disease.本数据集旨在支持复杂果园环境下黄瓜叶片异常的自动检测与定位。本研究的假设为：尽管存在光照、拍摄角度、尺度、背景杂乱、叶片重叠以及生育期等诸多变量，仍可借助基于深度学习的分类与目标检测模型，从田间图像中识别出肉眼可见的病害损伤与叶片异常着色。 数据集共包含500张黄瓜叶片图像：250张健康叶片图像与250张带有病害症状或异常着色的叶片图像。所有图像均采集自真实种植环境，随后依据叶片状态进行检查与整理。标注子集附带包含矩形边界框（bounding box）标注的文本标签文件。每条标注行包含5个数值：类别标识符、边界框中心的横纵坐标，以及框的宽度与高度。类别0代表异常着色——即除健康绿色之外的可见叶片颜色，例如黄化或褐变；类别1代表可见的病害相关损伤。单张图像可包含多条标注，因为同一片叶片上可出现多处受影响区域。本数据集共包含2705个标注区域：1741个着色异常区域与964个病害损伤区域。 标注分布显示，异常着色的出现频率高于直接可识别的损伤区域。这反映了黄瓜叶片病害的异质性视觉表现，使得本数据集可用于研究整体变色与局部物理症状。健康叶片图像可用于图像级二分类任务，而带标注的图像可用于目标检测与矩形区域定位任务。当前版本的数据集仅提供边界框而非像素级分割掩码（pixel-level segmentation masks），因此不应被视为传统语义分割或实例分割的真实标签（ground truth）。 研究人员可利用本数据集训练、验证并对比深度学习架构，以完成健康与受感染叶片分类、着色异常检测、病害损伤定位、迁移学习、数据增强以及自然种植环境下的鲁棒性评估等任务。在开展模型开发前，使用者应验证图像与标签的对应关系，保留类别映射关系，并创建不重叠的训练、验证与测试集划分。由于标注描述的是可见表型类别而非病原特异性诊断，模型预测应被解读为异常着色或损伤的指示，而非特定黄瓜病害的确诊结果。