dc.contributor.author | Ferrer-Ferrer, Mar | |
dc.contributor.author | Ruiz-Hidalgo, Javier | |
dc.contributor.author | Gregorio, Eduard | |
dc.contributor.author | Vilaplana, Verónica | |
dc.contributor.author | Morros, Josep-Ramon | |
dc.contributor.author | Gené-Mola, Jordi | |
dc.contributor.other | Producció Vegetal | ca |
dc.date.accessioned | 2023-10-04T17:06:15Z | |
dc.date.available | 2023-10-04T17:06:15Z | |
dc.date.issued | 2023-08-06 | |
dc.identifier.citation | Ferrer-Ferrer, Mar, Javier Ruiz-Hidalgo, Eduard Gregorio, Verónica Vilaplana, Josep-Ramon Morros, and Jordi Gené-Mola. 2023. "Simultaneous Fruit Detection And Size Estimation Using Multitask Deep Neural Networks". Biosystems Engineering 233: 63-75. doi:10.1016/j.biosystemseng.2023.07.010. | ca |
dc.identifier.issn | 1537-5110 | ca |
dc.identifier.uri | http://hdl.handle.net/20.500.12327/2400 | |
dc.description.abstract | The measurement of fruit size is of great interest to estimate the yield and predict the harvest resources in advance. This work proposes a novel technique for in-field apple detection and measurement based on Deep Neural Networks. The proposed framework was trained with RGB-D data and consists of an end-to-end multitask Deep Neural Network architecture specifically designed to perform the following tasks: 1) detection and segmentation of each fruit from its surroundings; 2) estimation of the diameter of each detected fruit. The methodology was tested with a total of 15,335 annotated apples at different growth stages, with diameters varying from 27 mm to 95 mm. Fruit detection results reported an F1-score for apple detection of 0.88 and a mean absolute error of diameter estimation of 5.64 mm. These are state-of-the-art results with the additional advantages of: a) using an end-to-end multitask trainable network; b) an efficient and fast inference speed; and c) being based on RGB-D data which can be acquired with affordable depth cameras. On the contrary, the main disadvantage is the need of annotating a large amount of data with fruit masks and diameter ground truth to train the model. Finally, a fruit visibility analysis showed an improvement in the prediction when limiting the measurement to apples above 65% of visibility (mean absolute error of 5.09 mm). This suggests that future works should develop a method for automatically identifying the most visible apples and discard the prediction of highly occluded fruits. | 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 PID2020-117142 GB-I00 [DeeLight project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union). 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. | ca |
dc.format.extent | 13 | ca |
dc.language.iso | eng | ca |
dc.publisher | Elsevier | ca |
dc.relation.ispartof | Biosystems Engineering | ca |
dc.rights | Attribution 4.0 International | ca |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | Simultaneous fruit detection and size estimation using multitask deep neural networks | 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 | 631 | ca |
dc.identifier.doi | https://doi.org/10.1016/j.biosystemseng.2023.07.010 | ca |
dc.contributor.group | Ús Eficient de l'Aigua en Agricultura | ca |