{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:39:21Z","timestamp":1771468761499,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Agriculture"],"abstract":"<jats:p>Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards\u2014Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.<\/jats:p>","DOI":"10.3390\/agriculture11030208","type":"journal-article","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T21:58:07Z","timestamp":1614895087000},"page":"208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9999-1550","authenticated-orcid":false,"given":"Daniel Queir\u00f3s","family":"da Silva","sequence":"first","affiliation":[{"name":"INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal"},{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6909-0209","authenticated-orcid":false,"given":"Andr\u00e9 Silva","family":"Aguiar","sequence":"additional","affiliation":[{"name":"INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal"},{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-6113","authenticated-orcid":false,"given":"Filipe Neves","family":"dos Santos","sequence":"additional","affiliation":[{"name":"INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0317-4714","authenticated-orcid":false,"given":"Armando Jorge","family":"Sousa","sequence":"additional","affiliation":[{"name":"INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal"},{"name":"Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0062-6623","authenticated-orcid":false,"given":"Danilo","family":"Rabino","sequence":"additional","affiliation":[{"name":"Institute of Sciences and Technologies for Sustainable Energy and Mobility, National Research Council (STEMS-CNR), 10135 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6843-2016","authenticated-orcid":false,"given":"Marcella","family":"Biddoccu","sequence":"additional","affiliation":[{"name":"Institute of Sciences and Technologies for Sustainable Energy and Mobility, National Research Council (STEMS-CNR), 10135 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6619-0041","authenticated-orcid":false,"given":"Giorgia","family":"Bagagiolo","sequence":"additional","affiliation":[{"name":"Institute of Sciences and Technologies for Sustainable Energy and Mobility, National Research Council (STEMS-CNR), 10135 Turin, Italy"}]},{"given":"Marco","family":"Delmastro","sequence":"additional","affiliation":[{"name":"Institute of Sciences and Technologies for Sustainable Energy and Mobility, National Research Council (STEMS-CNR), 10135 Turin, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, G., Wang, X., Ding, Y., Lu, W., and Sun, Y. (2019). Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry. Agronomy, 9.","DOI":"10.3390\/agronomy9110774"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Escol\u00e0, A., Mart\u00ednez-Casasnovas, J.A., Rufat, J., Arn\u00f3, J., Arbon\u00e9s, A., Seb\u00e9, F., Pascual, M., Gregorio, E., and Rosell-Polo, J.R. (2017). Mobile terrestrial laser scanner applications in precision fruticulture\/horticulture and tools to extract information from canopy point clouds. Precis. Agric., 18.","DOI":"10.1007\/s11119-016-9474-5"},{"key":"ref_3","first-page":"299","article-title":"Leaf area index estimation in vineyards from Uav hyperspectral data, 2D image mosaics and 3D canopy surface models","volume":"XL-1\/W4","author":"Kalisperakis","year":"2015","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Anifantis, A.S., Camposeo, S., Vivaldi, G.A., Santoro, F., and Pascuzzi, S. (2019). Comparison of UAV Photogrammetry and 3D Modeling Techniques with Other Currently Used Methods for Estimation of the Tree Row Volume of a Super-High-Density Olive Orchard. Agriculture, 9.","DOI":"10.3390\/agriculture9110233"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Comba, L., Biglia, A., Ricauda Aimonino, D., Tortia, C., Mania, E., Guidoni, S., and Gay, P. (2020). Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery. Precis. Agric., 21.","DOI":"10.1007\/s11119-019-09699-x"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.3390\/rs5052164","article-title":"Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud","volume":"5","author":"Mathews","year":"2013","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/TGRS.2012.2205003","article-title":"Retrieval of Effective Leaf Area Index in Heterogeneous Forests With Terrestrial Laser Scanning","volume":"51","author":"Zheng","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Khaliq, A., Comba, L., Biglia, A., Ricauda Aimonino, D., Chiaberge, M., and Gay, P. (2019). Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment. Remote Sens., 11.","DOI":"10.3390\/rs11040436"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4225","DOI":"10.1109\/JSTARS.2017.2711482","article-title":"Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery","volume":"10","author":"Ma","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.biosystemseng.2020.05.013","article-title":"Semantic interpretation and complexity reduction of 3D point clouds of vineyards","volume":"197","author":"Comba","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1017\/S0373463318000838","article-title":"Assessment of the Positioning Accuracy of DGPS and EGNOS Systems in the Bay of Gdansk using Maritime Dynamic Measurements","volume":"72","author":"Specht","year":"2019","journal-title":"J. Navig."},{"key":"ref_12","unstructured":"(2020, February 04). Robot Operating System (ROS). Available online: https:\/\/www.ros.org\/."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., and Frahm, J. (2016, January 27\u201330). Structure-from-Motion Revisited. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_14","unstructured":"Fuhrmann, S., Langguth, F., and Goesele, M. (2014, January 6\u20138). MVE\u2014A Multi-View Reconstruction Environment. Proceedings of the Eurographics Workshop on Graphics and Cultural Heritage (GCH), Darmstadt, Germany."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kerautret, B., Colom, M., and Monasse, P. (2017). OpenMVG: Open Multiple View Geometry. Reproducible Research in Pattern Recognition, Springer International Publishing.","DOI":"10.1007\/978-3-319-56414-2"},{"key":"ref_16","unstructured":"(2020, February 04). PIX4D. Available online: https:\/\/www.pix4d.com\/."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.isprsjprs.2019.02.015","article-title":"Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets","volume":"151","author":"Xu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A method for registration of 3-D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.isprsjprs.2020.03.013","article-title":"Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark","volume":"163","author":"Dong","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D is here: Point Cloud Library (PCL). Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10514-012-9321-0","article-title":"OctoMap: An efficient probabilistic 3D mapping framework based on octrees","volume":"34","author":"Hornung","year":"2013","journal-title":"Auton. Robot."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, Y., and Gu, R. (2020). Research Status and Prospects on Plant Canopy Structure Measurement Using Visual Sensors Based on Three-Dimensional Reconstruction. Agriculture, 10.","DOI":"10.3390\/agriculture10100462"},{"key":"ref_23","unstructured":"(2020, February 04). CloudCompare. Available online: http:\/\/www.cloudcompare.org\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.dib.2018.02.012","article-title":"Dataset of spray deposit distribution in vine canopy for two contrasted performance sprayers during a vegetative cycle associated with crop indicators (LWA and TRV)","volume":"18","author":"Codis","year":"2018","journal-title":"Data Brief"}],"container-title":["Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0472\/11\/3\/208\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:32:39Z","timestamp":1760160759000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0472\/11\/3\/208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":24,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["agriculture11030208"],"URL":"https:\/\/doi.org\/10.3390\/agriculture11030208","relation":{},"ISSN":["2077-0472"],"issn-type":[{"value":"2077-0472","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,4]]}}}