Bacterial-fungicidal vine disease detection with proximal aerial images
Delia Elena Székely,
Darius Dobra,
Alexandra Elena Dobre,
Victor Domşa,
Bogdan Gabriel Drăghici,
Tudor-Alexandru Ileni,
Robert Konievic,
Szilárd Molnár,
Paul Sucala,
Elena Zah,
Adrian Sergiu Darabant,
Attila Sándor,
Levente Tamás
Affiliations
Delia Elena Székely
Department of Horticultural Sciences, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania
Darius Dobra
Computer Science, Babes Bolyai University, Cluj-Napoca, Romania
Alexandra Elena Dobre
Automation Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Victor Domşa
Automation Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Bogdan Gabriel Drăghici
Automation Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Tudor-Alexandru Ileni
Computer Science, Babes Bolyai University, Cluj-Napoca, Romania
Robert Konievic
Automation Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Szilárd Molnár
Automation Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Paul Sucala
Automation Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Elena Zah
Automation Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Adrian Sergiu Darabant
Computer Science, Babes Bolyai University, Cluj-Napoca, Romania
Attila Sándor
Department of Parasitology and Parasitic Diseases, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania; Department of Parasitology and Zoology, University of Veterinary Medicine, Budapest, Hungary; HUN-REN-UVMB Climate Change, New Blood-Sucking Parasites and Vector-Borne Pathogens Research Group, Budapest, Hungary
Levente Tamás
Department of Horticultural Sciences, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania; Corresponding author.
Vine disease detection is considered one of the most crucial components in precision viticulture. It serves as an input for several further modules, including mapping, automatic treatment, and spraying devices. In the last few years, several approaches have been proposed for detecting vine disease based on indoor laboratory conditions or large-scale satellite images integrated with machine learning tools. However, these methods have several limitations, including laboratory-specific conditions or limited visibility into plant-related diseases. To overcome these limitations, this work proposes a low-altitude drone flight approach through which a comprehensive dataset about various vine diseases from a large-scale European dataset is generated. The dataset contains typical diseases such as downy mildew or black rot affecting the large variety of grapes including Muscat of Hamburg, Alphonse Lavallée, Grasă de Cotnari, Rkatsiteli, Napoca, Pinot blanc, Pinot gris, Chambourcin, Fetească regală, Sauvignon blanc, Muscat Ottonel, Merlot, and Seyve-Villard 18402. The dataset contains 10,000 images and more than 100,000 annotated leaves, verified by viticulture specialists. Grape bunches are also annotated for yield estimation. Further, tests were made against state-of-the-art detection methods on this dataset, focusing also on viable solutions on embedded devices, including Android-based phones or Nvidia Jetson boards with GPU. The datasets, as well as the customized embedded models, are available on the project webpage.2