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Abstract：
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.
摘要：
国家和国际Vitis品种目录可用作葡萄栽培中计算机视觉的图像数据集。这些数据库存档了几种葡萄品种和植物结构图像(例如叶，束，shoots).尽管这些档案代表了葡萄栽培中计算机视觉的潜在数据库，植物结构图像是单独获得的，大多数不是直接在葡萄园中获得的。定位计算机视觉模型将利用同一图像中的多个对象，允许更有效的培训。本图像和标签数据集被设计为克服这些限制并为白葡萄品种中的多个簇识别提供合适的图像。在意大利六个不同位置的不同物候阶段的垂直拍摄位置葡萄园中，从后来的视野中获取了一组373张图像。然后以YOLO标签格式标记图像。数据集在图像和标签方面都可用。字段中计数的实际束数，并且在该数据集中的一组图像中记录图像中可见的束的数量(未被其他藤蔓结构覆盖)。
