Abdelghafour Florent, Keresztes Barna, Deshayes Aymeric, Germain Christian, Da Costa Jean-Pierre
ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France.
Univ. Bordeaux, IMS UMR 5218, F-33405 Talence, France.
Data Brief. 2021 Jun 29;37:107250. doi: 10.1016/j.dib.2021.107250. eCollection 2021 Aug.
This article introduces a dataset of high-resolution colour images of grapevines. It contains 99 images acquired in the vineyard from a cruising tractor. Each image includes the full foliage of a grapevine plant. These images display a diverse range of symptoms caused by the grapevine downy mildew (), a major fungal disease. The dataset also includes various confounding factors, . anomalies that are not related to the disease. These anomalies are the natural and common phenomena affecting vineyards such as results of mechanical wounds, necroses, chemical burns or yellowing and discolorations due to nutritional or hydric deficiencies. Images were acquired in-situ on "Le Domaine de la Grande Ferrade" a public experimental facility of INRAE, in the area of Bordeaux. Acquisitions took place at early fruiting stages (BBCH 75-79) corresponding to the main sanitary pressure during growth. The acquisition device, embedded on a vine tractor, is composed of an industrial colour camera synchronised with powerful flashes. The purpose of this device is to produce a "day for night" effect that mitigates the variation of sunlight. It enables to homogenise images acquired during different weathers and time of the day and to ensure that the foreground (containing foliage) displays appropriate brightness, with minimum shadows while the background is darker. The images of the dataset were annotated manually by photo-interpretation with a careful review of expertise regarding phytopathology and physiological disorders. The annotation process consists in associating pixels with a class that defines its membership to a type of organ and its physiological state. Pixels from healthy, symptomatic or abnormal grapevine tissues were labelled into seven classes: "Limbus", "Leaf edges", "Berries", "Stems", "Foliar mildew", "Berries mildew" and "Anomalies". The annotation is achieved with the GIMP2 software as mask images where the value attributed to each pixel corresponds to one of the seven considered classes.
本文介绍了一个葡萄藤高分辨率彩色图像数据集。它包含从一辆巡航拖拉机在葡萄园采集的99张图像。每张图像都包含一株葡萄藤植物的完整枝叶。这些图像展示了由葡萄霜霉病（一种主要的真菌病害）引起的各种不同症状。该数据集还包括各种混杂因素，即与疾病无关的异常情况。这些异常是影响葡萄园的自然且常见的现象，如机械损伤、坏死、化学灼伤的结果，或由于营养或水分缺乏导致的发黄和变色。图像是在波尔多地区法国农业环境与农业工程研究院（INRAE）的公共实验设施“大费拉德庄园”现场采集的。采集在早期结果阶段（BBCH 75 - 79）进行，这对应于生长期间的主要卫生压力期。采集设备安装在葡萄藤拖拉机上，由一台与强力闪光灯同步的工业彩色相机组成。该设备的目的是产生一种“日转夜”效果，以减轻阳光的变化。它能够使在不同天气和一天中的不同时间采集的图像均匀化，并确保前景（包含枝叶）显示出适当的亮度，阴影最少，而背景较暗。该数据集的图像通过照片判读进行人工标注，并仔细审查了有关植物病理学和生理失调的专业知识。标注过程包括将像素与一个类别相关联，该类别定义了其属于一种器官类型及其生理状态。来自健康、有症状或异常葡萄藤组织的像素被标记为七个类别：“边缘”、“叶边缘”、“浆果”、“茎”、“叶部霜霉病”、“浆果霜霉病”和“异常”。标注是使用GIMP2软件作为掩码图像完成的，其中赋予每个像素的值对应于所考虑的七个类别之一。
