Abstract
This paper presents a 3D plant segmentation method with an emphasis on segmentation of the leaves. This method is part of a 3D plant phenotyping project with a main objective that deals with the development of the leaf area over time. First, a 3D point cloud of a plant is obtained with Structure from Motion technique and the cloud is then segmented into the main components of a plant: the stem and the leaves. As the main objective is to measure leaf area over time, an emphasis was placed on accurate segmentation and the labelling of the leaves. This article presents an original approach which starts by finding the stem in a 3D point cloud and then the leaves. Moreover, this method relies on the model of a plant as well as the agronomic rules to affect a unique label that do not change over time. This method is evaluated using two morphologically distinct plants, sunflower and sorghum.
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References
Dhondt, S., Wuyts, N., Inzé, D.: Cell to whole-plant phenotyping: the best is yet to come. Trends Plant Sci. 18(8), 428–439 (2013)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)
Fiorani, F., Schurr, U.: Future scenarios for plant phenotyping. Ann. Rev. Plant Biol. 64, 267–291 (2013)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in Computer Vision: Issues, Problems, Principles, and Paradigms, pp. 726–740. Morgan Kaufmann Publishers Inc. (1987)
Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Towards internet-scale multi-view stereo. In: CVPR (2010)
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1362–1376 (2010)
Gélard, W., Devy, M., Herbulot, A., Burger, P.: Model-based segmentation of 3D point clouds for phenotyping sunflower plants. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 459–467 (2017)
Itseez: Open source computer vision library (2015). https://github.com/itseez/opencv
Jay, S., Rabatel, G., Hadoux, X., Moura, D., Gorretta, N.: In-field crop row phenotyping from 3D modeling performed using structure from motion. Comput. Electron. Agric. 110, 70–77 (2015)
Louarn, G., Carré, S., Boudon, F., Eprinchard, A., Combes, D.: Characterization of whole plant leaf area properties using laser scanner point clouds. In: Fourth International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (2012)
McCormick, R.F., Truong, S.K., Mullet, J.E.: 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture. Plant Physiol. 172(2), 823–834 (2016)
Moulon, P., Monasse, P., Marlet, R., et al.: OpenMVG: an open multiple view geometry library (2013). https://github.com/openMVG/openMVG
Papon, J., Abramov, A., Schoeler, M., Wörgötter, F.: Voxel cloud connectivity segmentation - supervoxels for point clouds. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)
Paproki, A., Sirault, X., Berry, S., Furbank, R., Fripp, J.: A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol. 12(1), 63 (2012)
Paulus, S., Dupuis, J., Mahlein, A.K., Kuhlmann, H.: Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinf. 14(1), 1–12 (2013)
Rey, H., Dauzat, J., Chenu, K., Barczi, J.F., Dosio, G.A.A., Lecoeur, J.: Using a 3-D virtual sunflower to simulate light capture at organ, plant and plot levels: contribution of organ interception, impact of heliotropism and analysis of genotypic differences. Ann. Bot. 101(8), 1139–1151 (2008)
Rusu, R.B.: Semantic 3D object maps for everyday manipulation in human living environments. Ph.D. thesis, Computer Science department, Technische Universitaet Muenchen, Germany (2009)
Santos, T.T., Oliveira, A.A.: Image-based 3D digitizing for plant architecture analysis and phenotyping. In: Workshop on Industry Applications (WGARI) (2012)
Wahabzada, M., Paulus, S., Kersting, K., Mahlein, A.K.: Automated interpretation of 3D laserscanned point clouds for plant organ segmentation. BMC Bioinf. 16(1), 1–11 (2015)
Acknowledgements
The authors would like to thank Philippe Burger, Nicolas Langlade and Pierre Casadebaig from INRA, Toulouse, for their participation to this work, through a joint project about high throughput phenotyping of sunflowers and the French National Research Agency (ANR) through the project SUNRISE.
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Gélard, W. et al. (2017). Leaves Segmentation in 3D Point Cloud. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_56
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DOI: https://doi.org/10.1007/978-3-319-70353-4_56
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