An annotated image dataset of downy mildew symptoms on Merlot grape variety

Published: 9 March 2021| Version 1 | DOI: 10.17632/yxf2yv5ymt.1
Florent abdelghafour,


This is a dataset of high-resolution colour images of grapevines. It contains 99 images acquired in the vineyard from a cruising tractor. Each image include the full foliage of a grapevine plant. These images display a diverse range of symptoms caused by the grapevine downy mildew (Plasmopara viticola), a major fungal disease. The dataset also includes various confounding factors, i.e. anomalies which 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.


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Images were acquired at Le Domaine de la Grande Ferrade, a public experimental facility of INRAE ((the French) National Institute of Agriculture, Food and Environmental Research) in the area of Bordeaux. Images were taken on two 0.3 ha plots planted with the red wine grape variety “Merlot Noir”. One of the plots is cultivated with integrated crop protection and the other according to organic standards. For both plots, phytosanitary inputs are reduced to 50% of the conventional prescribed dose. The plants were affected only by downy mildew and abiotic stresses. At the end of July 2018, the plots were extensively photographed weekly with examples of healthy vinestocks and examples of vinestocks with early and late symptoms corresponding to phenological stages between BBCH ([1] Lorenz et al., 1995) (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie) 75 (berries pea-sized, bunches hang) and 79 (majority of berries touching). The imaging system is composed of a 5 Mpx industrial Basler Ace (acA2500-14gc GigE,Basler AG, Ahrensburg, Germany) global shutter RGB camera with a 55° horizontal field of view lens. To overcome the weather- and time-dependent variations of illuminations in outdoor environments, the imaging system includes a high-power 58GN xenon flash (Neewer speedlite 750ii, Shenzhen Neewer Technology Co., Guangdong, China) used with a short exposure time (250–300 μs). All the components are powered by a 12 V battery. The device is equipped with an on-board industrial computer that simultaneously controls the shooting of the camera and the trigger of the flash, and stores the acquired image data. The computer is built around a low consumption 4-core ARM chip robust to vibrations and watertight. The device is embedded on a vineyard tractor at 70 cm above ground and at 50cm from the target. At this distance, each image (2592×2048 px) covers a 1.3 m² area which enables to approximately capture a vinestock and its full canopy at a resolution of 4 px·mm−1.


Bordeaux INP, Laboratoire de l'integration du materiau au systeme, Bordeaux Sciences Agro, Irstea Centre de Montpellier


Image Processing, Plant Pathology, Precision Agriculture