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wGrapeUNIPD-DL: An open dataset for white grape bunch detection.
Sozzi, Marco; Cantalamessa, Silvia; Cogato, Alessia; Kayad, Ahmed; Marinello, Francesco.
Affiliation
  • Sozzi M; Department of Land Environment Agriculture and Forestry, University of Padova, Legnaro 35020, Italy.
  • Cantalamessa S; Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Legnaro 35020, Italy.
  • Cogato A; Department of Agricultural and Environmental Sciences, University of Udine, Udine 33100, Italy.
  • Kayad A; Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, United States.
  • Marinello F; Department of Land Environment Agriculture and Forestry, University of Padova, Legnaro 35020, Italy.
Data Brief ; 43: 108466, 2022 Aug.
Article in En | MEDLINE | ID: mdl-35873279
ABSTRACT
National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Data Brief Year: 2022 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Data Brief Year: 2022 Document type: Article