Phenotyping of poplar seedling leaves based on a 3D visualization method
Abstract
Keywords: NURBS, poplar seedling, 3D visualization, leaf geometrical characteristics
DOI: 10.25165/j.ijabe.20181106.4110
Citation: Hu C H, Li P P, Pan Z. Phenotyping of poplar seedling leaves based on a 3D visualization method. Int J Agric & Biol Eng, 2018; 11(6): 145–151.
Keywords
Full Text:
PDFReferences
Granier C, Tardieu F. Multi-scale phenotyping of leaf expansion in response to environmental changes: the whole is more than the sum of parts. Plant Cell Environ., 2009; 32: 1175–1184.
Kumar P, Huang C, Cai J, Miklavcic S J. Root phenotyping by root tip detection and classification through statistical learning. Plant Soil, 2014; 380: 193–209.
Muraya M M, Chu J, Zhao Y, Junker A, Klukas C, Reif J C, et al. Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non-invasive phenotyping. Plant Journal for Cell & Molecular Biology, 2017; 89(2): 366–380.
Monforte A J, Diaz A I, Caño-Delgado A, van der Knaap E. The genetic basis of fruit morphology in horticultural crops: Lessons from tomato and melon. Journal of Experimental Botany, 2014; 65(16): 4625.
Zhao C, Gao X, Ma R, Chen L, Chen T, Ren J. Responses of and seedlings to different draught stress of soil in ecophysiological characteristics. Journal of Glaciology & Geocryology, 2012; 34(1): 147–154.
An N, Palmer C M, Baker R L, Cody Markelz R J, Ta J, Covington M F, et al. Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area. Computers & Electronics in Agriculture, 2016; 127(C): 376–394.
Lu H, Tang L, Whitham S A, Mei Y. A robotic platform for corn seedling morphological traits characterization. Sensors, 2017; 17(9):2082.
Heuvelink E. Evaluation of a dynamic simulation model for tomato crop growth and development. Annals of Botany, 1998; 83: 413–422.
Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, et al. Review of methods for in situ leaf area index determination: Part I: theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 2004; 121(1-2): 19–35.
Fanourakis D, Briese C, Max J F J, Kleinen S, Putz A, Fiorani F, et al. Rapid determination of leaf area and plant height by using light curtain arrays in four species with contrasting shoot architecture. Plant Methods, 2014; 10(1): 1–9.
Yun T. A novel approach for retrieving tree leaf area from ground-based LiDAR, Remote Sensing, 2016; 8(11): 942–962.
Ran N L, Sagi F, Hanan E. Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points, Precision Agriculture, 2013; 14(6): 586–605.
Granier C, Aguirrezabal L, Chenu K, Cookson S J, Dauzat M, Hamard P, 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 Phytologist, 2006; 169(3): 623–35.
Tisné S, Serrand Y, Bach L, Gilbault E, Ben Ameur R, Balasse H, et al. Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. Plant Journal for Cell & Molecular Biology, 2013; 74(3): 534–44.
Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F. HTPheno: An image analysis pipeline for high-throughput plant phenotyping. BMC bioinformatics, 2011; 12(1): 148.
Yun T, Chen B, Li W, Sun Y, Xue L. Using point cloud data for tree organ classification and real leaf surface construction. Bulgarian Chemical Communications, 2017; 49(1): 288–296 .
Golbach F, Kootstra G, Damjanovic S, Otten G, van de Zedde R. Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping. Machine Vision and Applications, 2016; 27: 663–680.
Tanaka S, Kawamura K, Maki M, Muramoto Y, Yoshida K, Akiyama T. Spectral index for quantifying leaf area index of winter wheat by field hyperspectral measurements: a case study in gifu prefecture, central Japan. Remote Sensing, 2015; 7: 5329–5346.
Behmann J, Mahlein A K, Paulus S, Kuhlmann H, Oerke E C, Plümera L. Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping. ISPRS Journal of Photogrammetry and Remote Sensing, 2015; 106: 172–182.
Rosell Polo J R, Sanz R, Llorens J, Arnó J, Escolà A, Ribes-Dasi M, et al. A tractor-mounted scanning LiDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements. Biosystems Engineering, 2009; 102(2): 128–4134.
Yin K, Huang H, Long P, Gaissinski A, Gong M, Sharf A. Full 3D plant reconstruction via intrusive acquisition. Computer Graphics, 2016; 35(1): 272–284.
Kaminuma E, Heida N, Tsumoto Y, Yamamoto N, Goto N, Okamoto N, Konagaya A, Matsui M, Toyoda T. Automatic quantification of morphological traits via three-dimensional measurement of arabidopsis. Plant, 2004; 38: 358–365.
Lou L, Liu Y, Han J, Doonan J H. Accurate multi-view stereo 3D reconstruction for cost-effective plant phenotyping. International Conference Image Analysis & Recognition. Springer International Publishing, 2014; pp.349–356.
Biskup B, Scharr H, Schurr U, Rascher U. A stereo imaging system for measuring structural parameters of plant canopies. Plant, Cell and Environment, 2007; 30 (10): 1299–1308.
Chaivivatrakul S, Tang L, Dailey M N, Nakarmi A D. Automatic morphological trait characterization for corn plants via 3D holographic reconstruction. Computers and Electronics in Agriculture, 2014; 109(C): 109–123.
Klose R, Penlington J, Ruckelshausen A. Usability study of 3D time-of-flight cameras for automatic plant phenotyping. Bornimer Agrartechnische Berichte, 2009; 69: 93–105.
Grift T E, Oberti R. Development of low-cost, root collar diameter measurement devices for pine seedlings. Computers and Electronics in Agriculture, 2006; 52: 60–70.
Hu C H, You Y D, Li P P. 3D visualization of broad-leaved seedlings leaves combined depth image with digital image. International Conference on Computer Science and Communication Engineering (CSCE2015), Suzhou, China, 2015; 6: 244–249.
Zhang Y H, Liang T, Liu X J, Liu L, Cao W, Zhu Y. Modeling curve dynamics and spatial geometry characteristics of rice leaves. Journal of Integrative Agriculture, 2017; 16(10): 2177–2190.
Ghasemi H, Brighenti R, Zhuang X Y, Muthu J, Rabczuk T. Optimization of fiber distribution in fiber reinforced composite by using NURBS functions. Computational Materials Science, 2014; 83(8): 463–473.
Liu Z, Zhu Y, Li F, Jin G. Non-destructively predicting leaf area, leaf mass and specific leaf area based on a linear mixed-effect model for broadleaf species. Ecological Indicators, 2017; 78: 340–350.
Copyright (c) 2018 International Journal of Agricultural and Biological Engineering