wGrapeUNIPD-DL: an open dataset for white grape bunch detection

National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset.国家及国际葡萄属（Vitis）品种目录可作为葡萄栽培领域计算机视觉研究的图像数据集。此类数据库归档了多个葡萄品种的葡萄形态特征与物候期数据，以及植株结构图像（如叶片、果穗、新梢）。尽管此类档案具备成为葡萄栽培计算机视觉研究潜在数据库的潜力，但植株结构图像多为单张采集，且大多并非直接在葡萄园中获取。定位类计算机视觉模型若能利用同一张图像中的多个目标，将可实现更高效的模型训练。本图像与标注数据集旨在克服上述局限，为白葡萄品种的多果穗识别任务提供适配图像。研究团队在6处不同意大利产区的垂直新梢架型葡萄园的不同物候期阶段，采集了共计373张图像。随后采用YOLO标注格式对所有图像进行标注。本数据集同步开放图像与标注文件的获取渠道。本数据集的部分图像附带了田间实际统计的果穗总数，以及图像中可见（未被其他葡萄植株结构遮挡）的果穗数量记录。