HERMOS: An Annotated Image Dataset for Visual Detection of Grape Leaf Diseases Journal Title XX(X):1–14 The Author(s) 0000 Reprints and permission: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/ToBeAssigned www.sagepub.com/ SAGE Tu˘gba ¨ Ozacar 1 and ¨ Ov¨ un¸ c ¨ Ozt¨ urk 1 and Nurdan G¨ ung¨ or Sava¸ s 2 Abstract Powdery mildew, dead arm and vineyard downy mildew diseases are frequently seen in the vineyards in the Gediz River Basin, West Anatolia of Turkey. These diseases can be detected early using artificial intelligence (AI) based systems that can contribute to crop yields and also reduce the labor of the farmer and the amount of pesticides used. This article presents a dataset, namely Hermos, for use in such AI-based systems. Hermos contains four classes of grape leaf images; leaves with powdery mildew, leaves with dead arm, leaves with downy mildew and healthy leaves. We have currently 492 images and 13,913 labels in the dataset. We have published Hermos in the Linked Open Data (LOD) cloud in order to make it easier for consumers to access, process and manipulate the data. Keywords grape leaf disease, dataset, deep learning, image classification Introduction Diagnosing diseases at an early stage in the vineyards is of great importance. Early diagnosis ensures that the diseased areas are identified and healed quickly, 1 Department of Computer Engineering, Manisa Celal Bayar University, Muradiye, 45140, Manisa, Turkey 2 Manisa Viticulture Research Institute, Plant Protection Department, Yunusemre, 45125, Manisa, Turkey Corresponding author: Tu˘ gba ¨ Ozacar, Department of Computer Engineering, Manisa Celal Bayar University Email: tugba.ozacar@cbu.edu.tr Prepared using sagej.cls [Version: 2017/01/17 v1.20]