2026-04-18T05:59:40Zhttps://recercat.cat/oai/request
oai:recercat.cat:20.500.12327/26892025-10-22T11:27:20Zcom_2072_4428com_2072_4427col_2072_487898
RECERCAT author Gené-Mola, Jordi author Ferrer-Ferrer, Mar author Hemming, Jochen author van Dalfsen, Pieter author de Hoog, Dirk author Sanz-Cortiella, Ricardo author Rosell-Polo, Joan R. author Morros, Josep Ramon author Vilaplana, Verónica author Ruiz-Hidalgo, Javier author Gregorio, Eduard other Producció Vegetal group Ús Eficient de l'Aigua en Agricultura 2025-10-22T11:27:20Z 2025-10-22T11:27:20Z 2023-12-30 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. 2352-3409 http://hdl.handle.net/20.500.12327/2689 https://doi.org/10.1016/j.dib.2023.110000 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]. eng Attribution 4.0 International AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation info:eu-repo/semantics/article