{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:11:59Z","timestamp":1773771119786,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2015,11,18]],"date-time":"2015-11-18T00:00:00Z","timestamp":1447804800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["284443"],"award-info":[{"award-number":["284443"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2016,7]]},"DOI":"10.1007\/s00138-015-0727-5","type":"journal-article","created":{"date-parts":[[2015,11,18]],"date-time":"2015-11-18T14:13:48Z","timestamp":1447856028000},"page":"663-680","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping"],"prefix":"10.1007","volume":"27","author":[{"given":"Franck","family":"Golbach","sequence":"first","affiliation":[]},{"given":"Gert","family":"Kootstra","sequence":"additional","affiliation":[]},{"given":"Sanja","family":"Damjanovic","sequence":"additional","affiliation":[]},{"given":"Gerwoud","family":"Otten","sequence":"additional","affiliation":[]},{"given":"Rick","family":"van de Zedde","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,11,18]]},"reference":[{"issue":"12","key":"727_CR1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.tplants.2011.09.005","volume":"16","author":"RT Furbank","year":"2011","unstructured":"Furbank, R.T., Tester, M.: Phenomics\u2014technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16(12), 635\u2013644 (2011)","journal-title":"Trends Plant Sci."},{"issue":"6","key":"727_CR2","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.plantsci.2010.03.006","volume":"178","author":"\u00dc Kolukisaoglu","year":"2010","unstructured":"Kolukisaoglu, \u00dc., Thurow, K.: Future and frontiers of automated screening in plant sciences. Plant Sci. 178(6), 476\u2013484 (2010)","journal-title":"Plant Sci."},{"key":"727_CR3","doi-asserted-by":"crossref","unstructured":"Fiorani, F., Schurr, U.: Future scenarios for plant phenotyping. Annu. Rev. Plant Biol. 64, 267\u2013291 (2013)","DOI":"10.1146\/annurev-arplant-050312-120137"},{"issue":"11","key":"727_CR4","doi-asserted-by":"crossref","first-page":"20078","DOI":"10.3390\/s141120078","volume":"14","author":"L Li","year":"2014","unstructured":"Li, L., Zhang, Q., Huang, D.: A review of imaging techniques for plant phenotyping. Sensors 14(11), 20078\u201320111 (2014)","journal-title":"Sensors"},{"issue":"1","key":"727_CR5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13007-015-0050-1","volume":"11","author":"D Rousseau","year":"2015","unstructured":"Rousseau, D., et al.: Multiscale imaging of plants: current approaches and challenges. Plant Methods 11(1), 6 (2015)","journal-title":"Plant Methods"},{"issue":"1","key":"727_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-015-0043-0","volume":"11","author":"S Bergstrasser","year":"2015","unstructured":"Bergstrasser, S., et al.: HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging. Plant Methods 11(1), 1 (2015)","journal-title":"Plant Methods"},{"issue":"10","key":"727_CR7","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1111\/j.1365-3040.2007.01702.x","volume":"30","author":"B Biskup","year":"2007","unstructured":"Biskup, B., et al.: A stereo imaging system for measuring structural parameters of plant canopies. Plant Cell Environ. 30(10), 1299\u20131308 (2007)","journal-title":"Plant Cell Environ."},{"key":"727_CR8","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1071\/FP12019","volume":"39","author":"G Heijden van der","year":"2012","unstructured":"van der Heijden, G., et al.: SPICY: towards automated phenotyping of large pepper plants in the greenhouse. Funct. Plant Biol. 39, 870\u2013877 (2012)","journal-title":"Funct. Plant Biol."},{"key":"727_CR9","doi-asserted-by":"crossref","unstructured":"Eberius, M., Lima-Guerra, J.: High-throughput plant phenotyping\u2014data acquisition, transformation, and analysis. In: Bioinformatics: Tools and Applications, pp. 259\u2013278. Springer, New York (2009)","DOI":"10.1007\/978-0-387-92738-1_13"},{"issue":"1","key":"727_CR10","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1746-4811-7-2","volume":"7","author":"M Golzarian","year":"2011","unstructured":"Golzarian, M., et al.: Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7(1), 2 (2011)","journal-title":"Plant Methods"},{"key":"727_CR11","unstructured":"Imaging robots. http:\/\/www.psb.ugent.be\/infrastructure\/391-image-robots"},{"issue":"3","key":"727_CR12","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1111\/j.1469-8137.2005.01609.x","volume":"169","author":"C Granier","year":"2006","unstructured":"Granier, C., et al.: PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytol. 169(3), 623\u2013635 (2006)","journal-title":"New Phytol."},{"issue":"148","key":"727_CR13","first-page":"1","volume":"12","author":"A Hartmann","year":"2011","unstructured":"Hartmann, A., et al.: HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinf. 12(148), 1\u20139 (2011)","journal-title":"BMC Bioinf."},{"issue":"11","key":"727_CR14","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1071\/FP09095","volume":"36","author":"M Jansen","year":"2009","unstructured":"Jansen, M., et al.: Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Funct. Plant Biol. 36(11), 902\u2013914 (2009)","journal-title":"Funct. Plant Biol."},{"issue":"2","key":"727_CR15","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1111\/j.1469-8137.2007.02002.x","volume":"174","author":"A Walter","year":"2007","unstructured":"Walter, A., et al.: Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytol. 174(2), 447\u2013455 (2007)","journal-title":"New Phytol."},{"issue":"3","key":"727_CR16","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1111\/tpj.12131","volume":"74","author":"S Tisn\u00e9","year":"2013","unstructured":"Tisn\u00e9, S., et al.: Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. Plant J. 74(3), 534\u201344 (2013)","journal-title":"Plant J."},{"issue":"3","key":"727_CR17","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1007\/s00138-012-0434-4","volume":"24","author":"R Subramanian","year":"2013","unstructured":"Subramanian, R., Spalding, E., Ferrier, N.: A high throughput robot system for machine vision based plant phenotype studies. Mach. Vision Appl. 24(3), 619\u2013636 (2013)","journal-title":"Mach. Vision Appl."},{"key":"727_CR18","doi-asserted-by":"crossref","unstructured":"Minervini, M., Abdelsamea, M.M., Tsaftaris, S.A.: Image-based plant phenotyping with incremental learning and active contours. Ecol. Inf. 23 (2014)","DOI":"10.1016\/j.ecoinf.2013.07.004"},{"key":"727_CR19","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1071\/FP12052","volume":"39","author":"GA Pereyra-Irujo","year":"2012","unstructured":"Pereyra-Irujo, G.A., et al.: GlyPh: a low-cost platform for phenotyping plant growth and water use. Funct. Plant Biol. 39, 905\u2013913 (2012)","journal-title":"Funct. Plant Biol."},{"key":"727_CR20","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1071\/FP12018","volume":"39","author":"T Dornbusch","year":"2012","unstructured":"Dornbusch, T., et al.: Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis\u2014a novel phenotyping approach using laser scanning. Funct. Plant Biol. 39, 860\u2013869 (2012)","journal-title":"Funct. Plant Biol."},{"key":"727_CR21","doi-asserted-by":"crossref","unstructured":"Alenya, G., Dellen, B., Torras, C.: 3D modelling of leaves from color and ToF data for robotized plant measuring. In: 2011 IEEE International Conference on Robotics and Automation (ICRA) (2011)","DOI":"10.1109\/ICRA.2011.5980092"},{"key":"727_CR22","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.compag.2011.12.007","volume":"82","author":"Y Ch\u00e9n\u00e9","year":"2012","unstructured":"Ch\u00e9n\u00e9, Y., et al.: On the use of depth camera for 3D phenotyping of entire plants. Comput. Electron. Agric. 82, 122\u2013127 (2012)","journal-title":"Comput. Electron. Agric."},{"key":"727_CR23","first-page":"93","volume":"69","author":"R Klose","year":"2009","unstructured":"Klose, R., Penlington, J., Ruckelshausen, A.: Usability study of 3D time-of-flight cameras for automatic plant phenotyping. Bornimer Agrartechnische Berichte 69, 93\u2013105 (2009)","journal-title":"Bornimer Agrartechnische Berichte"},{"key":"727_CR24","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2005.02.015","volume":"49","author":"HJ Andersen","year":"2005","unstructured":"Andersen, H.J., Reng, L., Kirk, K.: Geometric plant properties by relaxed stereo vision using simulated annealing. Comput. Electron. Agric. 49, 219\u2013232 (2005)","journal-title":"Comput. Electron. Agric."},{"key":"727_CR25","doi-asserted-by":"crossref","unstructured":"Paproki, A., et al.: A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol. 12(63) (2012)","DOI":"10.1186\/1471-2229-12-63"},{"key":"727_CR26","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.3390\/s140203001","volume":"14","author":"S Paulus","year":"2014","unstructured":"Paulus, S., et al.: Low-cost 3D systems: suitable tools for plant phenotyping. Sensors 14, 3001\u20133018 (2014)","journal-title":"Sensors"},{"issue":"4","key":"727_CR27","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1104\/pp.114.248971","volume":"166","author":"MP Pound","year":"2014","unstructured":"Pound, M.P., et al.: Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol. 166(4), 1688\u20131698 (2014)","journal-title":"Plant Physiol."},{"issue":"3","key":"727_CR28","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1145\/1141911.1141929","volume":"25","author":"L Quan","year":"2006","unstructured":"Quan, L., et al.: Image-based plant modeling. ACM Trans. Graph. 25(3), 599\u2013604 (2006)","journal-title":"ACM Trans. Graph."},{"issue":"6","key":"727_CR29","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.gmod.2004.10.002","volume":"67","author":"A Baumberg","year":"2005","unstructured":"Baumberg, A., Lyons, A., Taylor, R.: 3D S.O.M.\u2014a commercial software solution to 3D scanning. Graph. Models 67(6), 476\u2013495 (2005)","journal-title":"Graph. Models"},{"key":"727_CR30","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","volume":"32","author":"Y Furukawa","year":"2010","unstructured":"Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1362\u20131376 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"727_CR31","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1104\/pp.108.134486","volume":"149","author":"B Biskup","year":"2009","unstructured":"Biskup, B., et al.: Diel growth cycle of isolated leaf discs analyzed with a novel, high-throughput three-dimensional imaging method is identical to that of intact leaves. Plant Physiol. 149(3), 1452\u20131461 (2009)","journal-title":"Plant Physiol."},{"key":"727_CR32","unstructured":"Baumgart, B.G.: Geometric Modeling for Computer Vision. PhD thesis, Stanford (1974)"},{"key":"727_CR33","doi-asserted-by":"crossref","unstructured":"Kim, Y., Aggarwal, J.: Rectangular parallelepiped coding: a volumetric representation of three dimensional objects. IEEE J. Robot. Autom. 2, pp. 127\u2013134 (1986)","DOI":"10.1109\/JRA.1986.1087056"},{"key":"727_CR34","doi-asserted-by":"crossref","unstructured":"Martin, W., Aggarwal, J.: Volumetric descriptions of objects from multiple views. IEEE Trans. Pattern Anal. Mach. Intell. 5(2), 150\u2013174 (1983)","DOI":"10.1109\/TPAMI.1983.4767367"},{"issue":"1","key":"727_CR35","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1006\/cviu.1993.1030","volume":"58","author":"R Szeliski","year":"1993","unstructured":"Szeliski, R.: Rapid octree construction from image sequences. Comput. Vision Graph. Image Process. Image Underst. 58(1), 23\u201332 (1993)","journal-title":"Comput. Vision Graph. Image Process. Image Underst."},{"key":"727_CR36","unstructured":"Laurentini, A.: The visual hull: a new tool for contour-based image understanding. In: Proceedings of the Seventh Scandinavian Conference on Image Analysis (1991)"},{"issue":"2","key":"727_CR37","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/34.273735","volume":"16","author":"A Laurentini","year":"1994","unstructured":"Laurentini, A.: The visual hull concept for Silhouette-based image understanding. IEEE Trans. Pattern Anal. Mach. Intell. 16(2), 150\u2013162 (1994)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"727_CR38","doi-asserted-by":"crossref","unstructured":"Koenderink, N.J.J.P., et al.: MARVIN: High speed 3D imaging for seedling classification. In: Seventh European Conference on Precision Agriculture, pp. 279 \u2013286 (2009)","DOI":"10.3920\/9789086866649_034"},{"key":"727_CR39","doi-asserted-by":"crossref","unstructured":"Kurillo, G., Zeyu, L., Bajcsy, R.: Wide-area external multi-camera calibration using vision graphs and virtual calibration object. In: IEEE Second ACM\/IEEE International Conference on Distributed Smart Cameras: Stanford (2008)","DOI":"10.1109\/ICDSC.2008.4635695"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-015-0727-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00138-015-0727-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-015-0727-5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T14:45:29Z","timestamp":1718203529000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00138-015-0727-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,18]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2016,7]]}},"alternative-id":["727"],"URL":"https:\/\/doi.org\/10.1007\/s00138-015-0727-5","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,11,18]]}}}