{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T03:57:51Z","timestamp":1772942271267,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2016,12,15]],"date-time":"2016-12-15T00:00:00Z","timestamp":1481760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.<\/jats:p>","DOI":"10.3390\/s16122136","type":"journal-article","created":{"date-parts":[[2016,12,15]],"date-time":"2016-12-15T10:53:16Z","timestamp":1481799196000},"page":"2136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions"],"prefix":"10.3390","volume":"16","author":[{"given":"Johann","family":"Rose","sequence":"first","affiliation":[{"name":"Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany"}]},{"given":"Anna","family":"Kicherer","sequence":"additional","affiliation":[{"name":"Julius K\u00fchn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany"}]},{"given":"Markus","family":"Wieland","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1941-150X","authenticated-orcid":false,"given":"Lasse","family":"Klingbeil","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany"}]},{"given":"Reinhard","family":"T\u00f6pfer","sequence":"additional","affiliation":[{"name":"Julius K\u00fchn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany"}]},{"given":"Heiner","family":"Kuhlmann","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2016,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.tplants.2011.09.005","article-title":"Phenomics\u2013technologies to relieve the phenotyping bottleneck","volume":"16","author":"Furbank","year":"2011","journal-title":"Trends Plant Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1007\/s00122-005-2016-6","article-title":"QTL analysis for fruit yield components in table grapes (Vitis vinifera)","volume":"111","author":"Fanizza","year":"2005","journal-title":"Theor. Appl. Genet."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nuske, S., Achar, S., Bates, T., Narasimhan, S., and Singh, S. (2011, January 25\u201330). Yield estimation in vineyards by visual grape detection. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6048830"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"423","DOI":"10.17660\/ActaHortic.2007.754.56","article-title":"Berry size and yield paradigms on grapes and wines quality","volume":"754","author":"Matthews","year":"2007","journal-title":"Acta Hortic."},{"key":"ref_5","first-page":"59","article-title":"Assessment of \u2018Tempranillo\u2019grapes quality in the vineyard by vitur score-sheet","volume":"42","author":"Tardaguila","year":"2008","journal-title":"J. Int. Sci. Vigne Vin"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1111\/j.1755-0238.2004.tb00022.x","article-title":"Yield prediction from digital image analysis: A technique with potential for vineyard assessments prior to harvest","volume":"10","author":"Dunn","year":"2004","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.compag.2014.07.006","article-title":"Counting red grapes in vineyards by detecting specular spherical reflection peaks in RGB images obtained at night with artificial illumination","volume":"108","author":"Font","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","unstructured":"Liu, S., Marden, S., and Whitty, M. (2013, January 2\u20134). Towards automated yield estimation in viticulture. Proceedings of the Australasian Conference on Robotics and Automation, Sydney, Australia."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"16988","DOI":"10.3390\/s121216988","article-title":"Grapevine yield and leaf area estimation using supervised classification methodology on RGB images taken under field conditions","volume":"12","author":"Diago","year":"2012","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compag.2013.11.008","article-title":"Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields","volume":"100","author":"Roscher","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.3390\/s150304823","article-title":"An automated field phenotyping pipeline for application in grapevine research","volume":"15","author":"Kicherer","year":"2015","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1002\/rob.21541","article-title":"Automated visual yield estimation in vineyards","volume":"31","author":"Nuske","year":"2014","journal-title":"J. Field Robot."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1071\/FP12018","article-title":"Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis a novel phenotyping approach using laser scanning","volume":"39","author":"Dornbusch","year":"2012","journal-title":"Funct. Plant Biol."},{"key":"ref_14","first-page":"563","article-title":"Terrestrial laser scanning of agricultural crops","volume":"37","author":"Lumme","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_15","unstructured":"Kraft, M., Regina, S., Freitas, D., and Munack, A. (2010, January 26\u201327). Test of a 3D time of flight camera for shape measurements of plants. Proceedings of the CIGR Workshop on Image Analysis in Agriculture, Budapest, Hungary."},{"key":"ref_16","unstructured":"Kizma, W., Foix, S., and Aleny\u00e0, G. (2012, January 16\u201318). Plant leaf analalysis using Time of Flight camera under sun, shadow and room conditions. Proceedings of the IEEE International Symposium on Robotic and Sensors Environments, Magdeburg, Germany."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.3390\/s140203001","article-title":"Low-cost 3D systems: Suitable tools for plant phenotyping","volume":"14","author":"Paulus","year":"2014","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9651","DOI":"10.3390\/s150509651","article-title":"Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level","volume":"15","author":"Rose","year":"2015","journal-title":"Sensors"},{"key":"ref_19","unstructured":"Jay, S., Rabatel, G., and Gorrettta, N. (2014, January 20\u201323). In-Field Crop Row Stereo-Reconsruction for Plant Phenotyping. Proceedings of the Second International Conference on Robotics and Associated High-Technologies and Equipment for Agriculture and forestry (RHEA), Bergamo, Italy."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.compag.2014.10.003","article-title":"Vineyard yield estimation by automatic 3D bunch modelling in field conditions","volume":"110","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1111\/phor.12063","article-title":"State of the art in high density image matching","volume":"29","author":"Remondino","year":"2014","journal-title":"Photogramm. Rec."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Weinmann, M., Schwartz, C., Ruiters, R., and Klein, R. (2011, January 16\u201319). A multi-camera, multi-projector super-resolution framework for structured light. Proceedings of the 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, Hangzhou, China.","DOI":"10.1109\/3DIMPVT.2011.57"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dey, D., Mummert, L., and Sukthankar, R. (2012, January 9\u201311). Classification of plant structures from uncalibrated image sequences. Proceedings of the IEEE Workshop on Applications of Computer Vision, Breckenridge, CO, USA.","DOI":"10.1109\/WACV.2012.6163017"},{"key":"ref_24","first-page":"192","article-title":"Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation","volume":"5","author":"Bora","year":"2015","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8284","DOI":"10.3390\/s150408284","article-title":"Vineyard Yield Estimation Based on the Analysis of High Resolution Images Obtained with Artificial Illumination at Night","volume":"15","author":"Font","year":"2015","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.tplants.2013.04.008","article-title":"Cell to whole-plant phenotyping: The best is yet to come","volume":"18","author":"Dhondt","year":"2013","journal-title":"Trends Plant Sci."},{"key":"ref_27","unstructured":"Danielgm CloudCompare. Available online: http:\/\/www.danielgm.net\/cc\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TVCG.2003.1175093","article-title":"Computing and Rendering Point Set Surfaces","volume":"9","author":"Alexa","year":"2003","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Paulus, S., Dupuis, J., Mahlein, A.K., and Kuhlmann, H. (2013). Surface feature based classiffication of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinform., 14.","DOI":"10.1186\/1471-2105-14-238"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.robot.2008.08.005","article-title":"Towards 3D Point cloud based object maps for household environments","volume":"56","author":"Rusu","year":"2008","journal-title":"Robot. Auton. Syst."},{"key":"ref_31","unstructured":"Sural, S., Qian, G., and Pramanik, S. (2002, January 22\u201325). Segmentation and histogram generation using the HSV color space for image retrieval. Proceedings of the International Conference on Image Processing, Rochester, NY, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.imavis.2012.04.004","article-title":"I2VM: Incremental import vector machines","volume":"30","author":"Roscher","year":"2012","journal-title":"Image Vis. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1198\/106186005X25619","article-title":"Kernel Logistic Regression and the Import Vector Machine","volume":"14","author":"Zhu","year":"2005","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_34","first-page":"249","article-title":"Supervised machine learning: A review of classification techniques","volume":"31","author":"Kotsiantis","year":"2007","journal-title":"Informatica"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Braun, A.C., Weidner, U., and Hinz, S. (2011, January 6\u20139). Support vector machines, import vector machines and relevance vector machines for hyperspectral classification\u2014A comparison. Proceedings of the 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal.","DOI":"10.1109\/WHISPERS.2011.6080861"},{"key":"ref_36","unstructured":"Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer."},{"key":"ref_37","first-page":"61","article-title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","volume":"10","author":"Platt","year":"1999","journal-title":"Adv. Large Margin Classif."},{"key":"ref_38","unstructured":"Delong, A. GCO. Available online: http:\/\/www.psi.toronto.edu\/~andrew\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11263-011-0437-z","article-title":"Fast Approximate Energy Minimization with Label Cost","volume":"96","author":"Delong","year":"2012","journal-title":"Int. J. Comput. Vis."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TPAMI.2004.1262177","article-title":"What energy functions can be minimized via graph cuts?","volume":"26","author":"Kolmogorov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"Kicherer, A., and Rose, J.C. Riesling Grapevine Canopy Images for 3D Reconstruction. Julius K\u00fchn-Institut. OpenAgrar Repository. Available online: https:\/\/openagrar.bmel-forschung.de\/receive\/openagrar_mods_00022533."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/MSP.2015.2405111","article-title":"Image Analysis: The New Bottleneck in Plant Phenotyping","volume":"32","author":"Minervini","year":"2015","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2830","DOI":"10.3390\/s130302830","article-title":"Breedvision\u2014A multi-sensor platform for non-destructive field-based phenotyping in plant breeding","volume":"13","author":"Busemeyer","year":"2013","journal-title":"Sensors"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.tplants.2013.09.008","article-title":"Field high-throughput phenotyping: The new crop breeding frontier","volume":"19","author":"Araus","year":"2014","journal-title":"Trends Plant Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/12\/2136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:28:35Z","timestamp":1760210915000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/12\/2136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,15]]},"references-count":44,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["s16122136"],"URL":"https:\/\/doi.org\/10.3390\/s16122136","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,15]]}}}