Vineyard dataset for automatic pruning based on main parts localization

This dataset provides a collection of labeled images related to different parts of the vineyard (trunk, shoot, and pruned shoot), collected in Badajoz, Spain, during 2021 and 2022. The labels were created with VGG Image Annotator (VIA) software. The dataset is particularly suitable for the development of object detection models, providing a solid basis for numerous applications in smart agriculture. Considering the growing importance of precision agriculture, this data provides a valuable starting point for implementing advanced solutions. In addition, the dataset has been used to train Mask R-CNN models for precise localization of plant parts, demonstrating its value for visual processing in agricultural settings.

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Publication Details

Journal
Data in Brief
Published
2025-01-27
DOI
https://doi.org/10.1016/j.dib.2025.111335
Citations
1
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
1.24

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Vineyard dataset for automatic pruning based on main parts localization

Horacio M. González–Velasco, Miguel Macías Macías, Antonio García‐Manso, Carlos J. García–Orellana et al.
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Data in Brief
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article

Vineyard dataset for automatic pruning based on main parts localization

Horacio M. González–Velasco, Miguel Macías Macías, Antonio García‐Manso, Carlos J. García–Orellana, Elia Pacioni, Eugenio Abengózar
article en
1 citations

Abstract

This dataset provides a collection of labeled images related to different parts of the vineyard (trunk, shoot, and pruned shoot), collected in Badajoz, Spain, during 2021 and 2022. The labels were created with VGG Image Annotator (VIA) software. The dataset is particularly suitable for the development of object detection models, providing a solid basis for numerous applications in smart agriculture. Considering the growing importance of precision agriculture, this data provides a valuable starting point for implementing advanced solutions. In addition, the dataset has been used to train Mask R-CNN models for precise localization of plant parts, demonstrating its value for visual processing in agricultural settings.

Data in BriefVol. 59
Red de Investigación en Sida (ES), Instituto de Arqueología-Mérida (ES), Universidad de Extremadura (ES)
Ministerio de Ciencia e Innovación
Openalex Percentile: Top 23%
Industrial Vision Systems and Defect Detection
1.24
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