AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation
Author
Ferrer-Ferrer, Mar
Hemming, Jochen
van Dalfsen, Pieter
de Hoog, Dirk
Sanz-Cortiella, Ricardo
Rosell-Polo, Joan R.
Morros, Josep Ramon
Vilaplana, Verónica
Ruiz-Hidalgo, Javier
Gregorio, Eduard
Publication date
2023-12-30ISSN
2352-3409
Abstract
The present dataset comprises a collection of RGB-D apple tree images that can be used to train and test computer vision-based fruit detection and sizing methods. This dataset encompasses two distinct sets of data obtained from a Fuji and an Elstar apple orchards. The Fuji apple orchard sub-set consists of 3925 RGB-D images containing a total of 15,335 apples annotated with both modal and amodal apple segmentation masks. Modal masks denote the visible portions of the apples, whereas amodal masks encompass both visible and occluded apple regions. Notably, this dataset is the first public resource to incorporate on-tree fruit amodal masks. This pioneering inclusion addresses a critical gap in existing datasets, enabling the development of robust automatic fruit sizing methods and accurate fruit visibility estimation, particularly in the presence of partial occlusions. Besides the fruit segmentation masks, the dataset also includes the fruit size (calliper) ground truth for each annotated apple. The second sub-set comprises 2731 RGB-D images capturing five Elstar apple trees at four distinct growth stages. This sub-set includes mean diameter information for each tree at every growth stage and serves as a valuable resource for evaluating fruit sizing methods trained with the first sub-set. The present data was employed in the research paper titled “Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation” [1].
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
633 - Field crops and their production
Pages
9
Publisher
Elsevier
Is part of
Data in Brief
Citation
Gené-Mola, Jordi, Mar Ferrer-Ferrer, Jochen Hemming, Pieter Van Dalfsen, Dirk De Hoog, Ricardo Sanz, Joan R. Rosell-Polo, et al. 2023. “AmodalAppleSize_RGB-D Dataset: RGB-D Images of Apple Trees Annotated with Modal and Amodal Segmentation Masks for Fruit Detection, Visibility and Size Estimation.” Data in Brief, December, 110000. https://doi.org/10.1016/j.dib.2023.110000.
Grant agreement number
MICIU/Programa Estatal de I+D+I orientada a los retos de la sociedad/RTI2018-094222-B-100/ES/Tecnologías de agricultura de precisión para optimizar el manejo de dosel foliar y la protección fitosanitaria sostenible en plantaciones de frutales/PAgFRUIT
MICINN/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/PID2021-126648OB-100/ES/Protección de cultivos de precisión para conseguir objetivos del Pacto Verde Europeo en uso eficiente y reducción de fitosanitarios mediate Agricultura de Precisión/PAgPROTECT
MICINN/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I y Programa Estatal de I+D+I orientada a los retos de la sociedad/PID2020-117142GB-I00/ES/ /DeeLight
FEDER/ / /EU/ /
Program
Ús Eficient de l'Aigua en Agricultura
This item appears in the following Collection(s)
- ARTICLES CIENTÍFICS [2649]
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/