Vineyard Image Dataset for Disease Detection, Cluster and Canopy Estimation

The dataset consists of vineyard images collected using the Viewer device during field trials of the EU Project OpenAgri, Sustainable Innovation Pilot 1. The color images were captured in a vineyard of the Savatiano variety located in Spata, Athens, in July 2025. Images were captured sequentially along vineyard rows and are organized into three subsets depending on the functionality used: yield estimation, disease detection, and canopy characterization. Each subset contains two subfolders: one containing the images and another containing the annotations in YOLO format. The annotation folders follow the standard YOLO dataset structure. Each image has a corresponding .txt file with the same filename containing the object annotations. Each line in the annotation file represents one labeled object and includes the class ID along with normalized spatial information relative to the image dimensions, provided either as bounding box parameters (center coordinates and width/height) for detection tasks or as segmentation polygons for canopy-related analysis such as vigor estimation. Additional configuration files such as obj.names, obj.data, and train.txt define the dataset classes, dataset paths, and the list of training images used for YOLO model training. If an image listed in the training dataset does not have a corresponding annotation entry, this indicates that no target object is present in that image. The yield estimation subset contains annotated images of Savatiano grape clusters at BBCH 75–79 stage, enabling cluster detection and counting for accurate yield prediction. The disease detection subset includes annotations of artificial targets representing downy mildew (Peronospora) symptoms placed on grapevine leaves for controlled detection experiments. The vigor detection subset includes annotations of vine structures such as trunk and foliage, enabling segmentation-based analysis of canopy geometry and spatial distribution. These annotations support the generation of vigor maps and variable-rate application (VRA) strategies by capturing variations in canopy development along vineyard rows.本数据集源自欧盟OpenAgri可持续创新试点1号项目田间试验期间，借助Viewer设备采集的葡萄园图像。该批次彩色图像拍摄于2025年7月，拍摄地点位于雅典斯帕塔（Spata）的Savatiano（萨瓦蒂亚诺）品种葡萄园。 图像沿葡萄园种植行连续拍摄，并根据所采用的功能分为三个子集：产量估算、病害检测与冠层表征。每个子集均包含两个子文件夹：其一用于存储原始图像，其二存储YOLO（You Only Look Once）格式的标注文件。 标注文件夹遵循标准YOLO数据集结构。每张图像对应一个同名的.txt格式标注文件，文件内每一行代表一个已标注目标，包含类别ID以及相对于图像尺寸归一化的空间信息：针对检测任务，信息格式为边界框参数（中心坐标与宽高）；针对冠层相关分析（如长势估算），则为分割多边形。 配套的配置文件如obj.names、obj.data与train.txt，分别用于定义数据集类别、数据集路径以及YOLO模型训练所用的训练图像列表。若训练数据集列表中的图像未匹配到对应标注条目，则表明该图像中不存在目标对象。 产量估算子集包含处于BBCH 75–79阶段的萨瓦蒂亚诺葡萄果簇标注图像，可用于果簇检测与计数，以实现精准的产量预测。 病害检测子集包含人工模拟霜霉病（Peronospora）症状的靶标标注，这类靶标被布置在葡萄叶片上，用于受控检测实验。 长势检测子集包含葡萄藤结构（如主干与叶片）的标注，支持基于分割的冠层几何形态与空间分布分析。此类标注通过捕捉葡萄园种植行沿线冠层发育的差异，可用于生成长势图谱与变量作业（variable-rate application, VRA）策略。