Hermos: An annotated image dataset for visual detection of grape leaf diseases

Özacar T., ÖZTÜRK Ö., Güngör Savaş N.

JOURNAL OF INFORMATION SCIENCE, vol.50, no.2, pp.394-403, 2024 (SCI-Expanded, SSCI, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 2
  • Publication Date: 2024
  • Doi Number: 10.1177/01655515221091892
  • Journal Name: JOURNAL OF INFORMATION SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, FRANCIS, IBZ Online, Periodicals Index Online, ABI/INFORM, Aerospace Database, Analytical Abstracts, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, EBSCO Education Source, Education Abstracts, Index Islamicus, Information Science and Technology Abstracts, INSPEC, Library and Information Science Abstracts, Library Literature and Information Science, Library, Information Science & Technology Abstracts (LISTA), Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.394-403
  • Keywords: Dataset, deep learning, grape leaf disease, image classification
  • Manisa Celal Bayar University Affiliated: Yes

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 labour 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.