Abstract
Plant phenotyping involves the measurement, ideally objectively, of characteristics or traits. Traditionally, this is either limited to tedious and sparse manual measurements, often acquired destructively, or coarse image-based 2D measurements. 3D sensing technologies (3D laser scanning, structured light and digital photography) are increasingly incorporated into mass produced consumer goods and have the potential to automate the process, providing a cost-effective alternative to current commercial phenotyping platforms. We evaluate the performance, cost and practicability for plant phenotyping and present a 3D reconstruction method from multi-view images acquired with a domestic quality camera. This method consists of the following steps: (i) image acquisition using a digital camera and turntable; (ii) extraction of local invariant features and matching from overlapping image pairs; (iii) estimation of camera parameters and pose based on Structure from Motion(SFM); and (iv) employment of a patch based multi-view stereo technique to implement a dense 3D point cloud. We conclude that the proposed 3D reconstruction is a promising generalized technique for the non-destructive phenotyping of various plants during their whole growth cycles.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alcantarilla, P.F., Nuevo, J., Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. In: British Machine Vision Conf., BMVC (2013)
Biskup, B., Scharr, H., Rascher, U.S.: A stereo imaging system for measuring structural parameters of plant canopies. Plant, Cell & Environment 30 (2007)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110, 346–359 (2008)
Cobb, J.N., DeClerck, G., Greenberg, A., Clark, R., McCouch, S.: Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics 126, 867–887 (2013)
Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Andwu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. of the ACM 45(6), 891–923 (1998)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Stewenius, H., Engels, C., Nister, D.: Recent developments on direct relative orientation. ISPRS Journal of Photogrammetry and Remote Sensing 60, 284–294 (2006)
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Tran. PAMI 32, 1362–1376 (2010)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2004)
Hern, C., Esteban, N., Schmitt, F.: Silhouette and stereo fusion for 3d object modeling. Comput. Vis. Image Underst. 96, 367–392 (2004)
Ivanov, N., et al.: Computer stereo plotting for 3-d reconstruction of a maize canopy. Agricultural and Forest Meteorology 75, 85–102 (1995)
Jancosek, M., Pajdla, T.: Multi-view reconstruction preserving weakly-supported surfaces. In: Proc. CVPR, pp. 3121–3128 (2011)
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 38, 358–365 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Morel, J.M., Yu, G.: Asift: A new framework for fully affine invariant image comparison. SIAM J. Img. Sci. 2(2), 438–469 (2009)
Organisation, E.P.S.: White paper of plant phenotyping. Tech. rep. (2010)
Paproki, A., Sirault, X., Berry, S., Furbank, R., Fripp, J.: A novel mesh processing based technique for 3d plant analysis. BMC Plant Biology 12 (2012)
Prince, S.J.D.: Computer Vision: Models, Learning, and Inference. Cambridge University Press (2012)
Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J.D., Kang, S.B.: Image-based plant modeling. ACM Trans. Graphics 25, 599–604 (2006)
Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: Proc. CVPR, vol. 1, pp. 519–528 (2006)
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: Exploring photo collections in 3d. In: Proc. SIGGRAPH, pp. 835–846 (2006)
Stephens, C.H.: Mike: A combined corner and edge detector. In: Proc. of Fourth Alvey Vision Conference, pp. 5–10 (1988)
Santos, T., Oliveira, A.: Image-based 3d digitizing for plant architecture analysis and phenotyping. In: Proc. Workshop on Industry Applications in SIB-GRAPI (2012)
Tola, E., Lepetit, V., Fua, P.: Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE Tran. PAMI 32(5), 815–830 (2010)
Vogiatzis, G., Hern, C., Esteban, N., Torr, P.H.S., Cipolla, R.: Multiview stereo via volumetric graph-cuts and occlusion robust photo-consistency. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2241–2246 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Lou, L., Liu, Y., Sheng, M., Han, J., Doonan, J.H. (2014). A Cost-Effective Automatic 3D Reconstruction Pipeline for Plants Using Multi-view Images. In: Mistry, M., Leonardis, A., Witkowski, M., Melhuish, C. (eds) Advances in Autonomous Robotics Systems. TAROS 2014. Lecture Notes in Computer Science(), vol 8717. Springer, Cham. https://doi.org/10.1007/978-3-319-10401-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-10401-0_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10400-3
Online ISBN: 978-3-319-10401-0
eBook Packages: Computer ScienceComputer Science (R0)