AKFruitYield: Modular benchmarking and video analysis software for Azure Kinect cameras for fruit size and fruit yield estimation in apple orchards
| dc.contributor.author | Miranda, Juan Carlos | |
| dc.contributor.author | Arnó Satorra, Jaume | |
| dc.contributor.author | Gené Mola, Jordi | |
| dc.contributor.author | Fountas, Spyros | |
| dc.contributor.author | Gregorio López, Eduard | |
| dc.date.accessioned | 2023-10-13T11:50:00Z | |
| dc.date.available | 2023-10-13T11:50:00Z | |
| dc.date.issued | 2023-10-06 | |
| dc.date.updated | 2023-10-13T11:50:00Z | |
| dc.description.abstract | AKFruitYield is a modular software that allows orchard data from RGB-D Azure Kinect cameras to be processed for fruit size and fruit yield estimation. Specifically, two modules have been developed: i) AK_SW_BENCHMARKER that makes it possible to apply different sizing algorithms and allometric yield prediction models to manually labelled color and depth tree images; and ii) AK_VIDEO_ANALYSER that analyses videos on which to automatically detect apples, estimate their size and predict yield at the plot or per hectare scale using the appropriate algorithms. Both modules have easy-to-use graphical interfaces and provide reports that can subsequently be used by other analysis tools. | |
| dc.description.sponsorship | This work was partly funded by the Department of Research and Universities of the Generalitat de Catalunya (grants 2017 SGR 646) and by the Spanish Ministry of Science and Innovation/AEI/10.13039/501100011033/ERDF (grant RTI2018–094222-B-I00 [PAgFRUIT project] and PID2021–126648OB-I00 [PAgPROTECT project]). The Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya and European Social Fund (ESF) are also thanked for financing Juan Carlos Miranda's pre-doctoral fellowship (2020 FI_B 00586). 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. The authors would also like to thank the Institut de Recerca i Tecnologia Agroalimentàries (IRTA) for allowing the use of their experimental fields, and in particular Dr. Luís Asín and Dr. Jaume Lordán who have contributed to the success of this work. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | https://doi.org/10.1016/j.softx.2023.101548 | |
| dc.identifier.idgrec | 033598 | |
| dc.identifier.issn | 2352-7110 | |
| dc.identifier.uri | https://repositori.udl.cat/handle/10459.1/464132 | |
| dc.language.iso | eng | |
| dc.publisher | Elsevier | |
| dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094222-B-I00/ES/TECNOLOGIAS DE AGRICULTURA DE PRECISION PARA OPTIMIZAR EL MANEJO DEL DOSEL FOLIAR Y LA PROTECCION FITOSANITARIA SOSTENIBLE EN PLANTACIONES FRUTALES/ | |
| dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2021-126648OB-I00/ES/PROTECCION DE CULTIVOS DE PRECISION PARA CONSEGUIR OBJETIVOS DEL PACTO VERDE EUROPEO EN USO EFICIENTE Y REDUCCION DE FITOSANITARIOS MEDIANTE AGRICULTURA DE PRECISION/ | |
| dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.1016/j.softx.2023.101548 | |
| dc.relation.ispartof | Softwarex, 2023, vol. 24, núm. 101548 | |
| dc.rights | cc-by (c) Miranda et al., 2023 | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | RGB-D cameras | |
| dc.subject | Fruit detection | |
| dc.subject | Fruit size | |
| dc.subject | Yield prediction | |
| dc.subject | Allometry | |
| dc.title | AKFruitYield: Modular benchmarking and video analysis software for Azure Kinect cameras for fruit size and fruit yield estimation in apple orchards | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | publishedVersion |