In-field apple size estimation using photogrammetry-derived 3D point clouds: Comparison of 4 different methods considering fruit occlusions
| dc.contributor.author | Gené Mola, Jordi | |
| dc.contributor.author | Sanz Cortiella, Ricardo | |
| dc.contributor.author | Rosell Polo, Joan Ramon | |
| dc.contributor.author | Escolà i Agustí, Alexandre | |
| dc.contributor.author | Gregorio López, Eduard | |
| dc.date.accessioned | 2021-09-27T11:32:30Z | |
| dc.date.issued | 2021-07-30 | |
| dc.date.updated | 2021-09-27T11:32:30Z | |
| dc.description.abstract | In-field fruit monitoring at different growth stages provides important information for farmers. Recent advances have focused on the detection and location of fruits, although the development of accurate fruit size estimation systems is still a challenge that requires further attention. This work proposes a novel methodology for automatic in-field apple size estimation which is based on four main steps: 1) fruit detection; 2) point cloud generation using structure-from-motion (SfM) and multi-view stereo (MVS); 3) fruit size estimation; and 4) fruit visibility estimation. Four techniques were evaluated in the fruit size estimation step. The first consisted of obtaining the fruit diameter by measuring the two most distant points of an apple detection (largest segment technique). The second and third techniques were based on fitting a sphere to apple points using least squares (LS) and M−estimator sample consensus (MSAC) algorithms, respectively. Finally, template matching (TM) was applied for fitting an apple 3D model to apple points. The best results were obtained with the LS, MSAC and TM techniques, which showed mean absolute errors of 4.5 mm, 3.7 mm and 4.2 mm, and coefficients of determination () of 0.88, 0.91 and 0.88, respectively. Besides fruit size, the proposed method also estimated the visibility percentage of apples detected. This step showed an of 0.92 with respect to the ground truth visibility. This allowed automatic identification and discrimination of the measurements of highly occluded apples. The main disadvantage of the method is the high processing time required (in this work 2760 s for 3D modelling of 6 trees), which limits its direct application in large agricultural areas. The code and the dataset have been made publicly available and a 3D visualization of results is accessible at http://www.grap.udl.cat/en/publications/apple_size_estimation_SfM. | |
| dc.description.sponsorship | This work was partly funded by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). The Spanish Ministry of Education is thanked for Mr. J. Gené’s pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) for their support during data acquisition. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | https://doi.org/10.1016/j.compag.2021.106343 | |
| dc.identifier.idgrec | 031560 | |
| dc.identifier.issn | 0168-1699 | |
| dc.identifier.uri | http://hdl.handle.net/10459.1/71933 | |
| dc.language.iso | eng | |
| dc.publisher | Elsevier | |
| dc.relation | info:eu-repo/grantAgreement/MINECO//AGL2013-48297-C2-2-R/ES/HERRAMIENTAS DE BASE FOTONICA PARA LA GESTION AGRONOMICA Y EL USO DE PRODUCTOS FITOSANITARIOS SOSTENIBLE EN CULTIVOS ARBOREOS EN EL MARCO DE LA AGRICULTURA DE PRECISION/ | |
| 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.isformatof | Reproducció del document publicat a: https://doi.org/10.1016/j.compag.2021.106343 | |
| dc.relation.ispartof | Computers and Electronics in Agriculture, 2021, vol. 188, núm. 106343 | |
| dc.relation.isreferencedby | https://doi.org/10.34810/data141 | |
| dc.rights | cc-by (c) Gené et al., 2021 | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Structure-from-motion | |
| dc.subject | fruit detection | |
| dc.subject | Fruit size | |
| dc.subject | Fruit visibility and occlusion | |
| dc.subject | Agricultural robotics | |
| dc.title | In-field apple size estimation using photogrammetry-derived 3D point clouds: Comparison of 4 different methods considering fruit occlusions | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion |