dc.contributor.author | Gené-Mola, Jordi | |
dc.contributor.author | Ferrer-Ferrer, Mar | |
dc.contributor.author | Hemming, Jochen | |
dc.contributor.author | van Dalfsen, Pieter | |
dc.contributor.author | de Hoog, Dirk | |
dc.contributor.author | Sanz-Cortiella, Ricardo | |
dc.contributor.author | Rosell-Polo, Joan R. | |
dc.contributor.author | Morros, Josep Ramon | |
dc.contributor.author | Vilaplana, Verónica | |
dc.contributor.author | Ruiz-Hidalgo, Javier | |
dc.contributor.author | Gregorio, Eduard | |
dc.contributor.other | Producció Vegetal | ca |
dc.date.accessioned | 2024-01-16T12:43:46Z | |
dc.date.available | 2024-01-16T12:43:46Z | |
dc.date.issued | 2023-12-30 | |
dc.identifier.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. | ca |
dc.identifier.issn | 2352-3409 | ca |
dc.identifier.uri | http://hdl.handle.net/20.500.12327/2689 | |
dc.description.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]. | ca |
dc.description.sponsorship | This work was partly funded by the Departament de Recerca i Universitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142GB-I00 [DeeLight project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union). Data presented in this paper is also part of a Public Private Partnership project Precisie Tuinbouw, WP Fruit 4.0 (PPS KV 1604-025) and financed by Topsector Tuinbouw & Uitgangsmateriaal and various private companies. The work of Jordi Gené Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU. We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) for their support during data acquisition. | ca |
dc.format.extent | 9 | ca |
dc.language.iso | eng | ca |
dc.publisher | Elsevier | ca |
dc.relation.ispartof | Data in Brief | ca |
dc.rights | Attribution 4.0 International | ca |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation | ca |
dc.type | info:eu-repo/semantics/article | ca |
dc.description.version | info:eu-repo/semantics/publishedVersion | ca |
dc.rights.accessLevel | info:eu-repo/semantics/openAccess | |
dc.embargo.terms | cap | ca |
dc.relation.projectID | 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 | ca |
dc.relation.projectID | 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 | ca |
dc.relation.projectID | 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 | ca |
dc.relation.projectID | FEDER/ / /EU/ / | ca |
dc.subject.udc | 633 | ca |
dc.identifier.doi | https://doi.org/10.1016/j.dib.2023.110000 | ca |
dc.contributor.group | Ús Eficient de l'Aigua en Agricultura | ca |