Plant 3D reconstruction based on LiDAR and multi-view sequence images

Jingwen Wu, Xinyu Xue, Songchao Zhang, Weicai Qin, Chen Chen, Tao Sun

Abstract


Abstract: The 3D reconstruction of plant based on LiDAR is the main way to obtain the spatial structure of plant rapidly, nondestructive and all-weather.  However, the influence of LiDAR instrument performance and field operation environment, the point cloud data obtained will lose the details of plant and reduce the accuracy of the model.  In this study, the three-dimensional point cloud of plants generated based on multi-view sequence images was taken as a reference.  The optimized Iterative Closest Point registration was adopted to calibrate the point cloud data from the LiDAR scanning to improve the detailed characteristics of the plants and establish a 3D model of plant.  At the same time, according to the measured plant phenotype parameters (leaf length, leaf width, leaf area, plant height), the accuracy of 3D model was evaluated.  The results showed that high accuracy of 3D reconstruction was obtained based on LiDAR and multi-view image sequence method.  There was a good agreement between measured and calculated leaf area, leaf length, leaf width and plant height with R2>0.8 for leaf area, RMSE<1.0 for leaf area, R2>0.85 for leaf length, R2>0.95 for leaf width.  There was no significant difference for each phenotypic parameter between measured and calculated data (ANOVA, P<0.05).  This method provides a technical reference for the research and application of LiDAR in fine modeling of field crops.

Keywords: LiDAR, multi-view sequence images, plant 3D reconstruction, accuracy evaluatione

DOI: 10.33440/j.ijpaa.20180101.0007

Citation:Wu J W, Xue X Y, Zhang S C, Qin W C, Chen C, Sun T.  Plant 3D reconstruction based on LiDAR and multi-view sequence images.  Int J Precis Agric Aviat, 2018; 1(1): 37–43.


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References


Xue X Y, Lan Y B. Agricultural Aviation Applications in USA. Transactions of the CSAM, 2013; 44(5): 194–201.

Zhai Changyuan, Zhu Ruixiang, Zhang Zuojing, et al. Status Analysis of Precision Pesticide Application Techniques. Journal of Agricultural Mechanization Research, 2010; 32(5): 9–12.

Fang Hui, Du Pengpeng,Hu Lingchao, et al. VTK-based plant 3D morphological visualization and registration. Transactions of the CSAE, 2013; 29(22): 180–188.

Liu Dan, Zhu Yeping, Liu Hailong, et al. Research Progress on 3D Plant Visualization. Journal of Agricultural Science and Technology, 2015; 17(1): 23–31.

Lindenmayer A. Mathematical models for cellular interactions in development. Journal of Theoretical Biology, 1968; 18(3): 280–299.

Fournier C, Andrieu B, Ljutovac S, et al. ADEL-Wheat: a 3D architectural model of wheat development// International Symposium on Plant Growth Modeling, Simulation, Visualization and Their Applications, 2003.

Wang Meili, He Dongjian. Visualized Simulation of Wheat Roots Based on L-System. Journal of Agricultural Mechanization Research, 2008; (3): 36–39.

Zhao Chunjiang, Wang Gongming, Guo Xinyu, et al. 3D visualization of corn root system based on interactive framework model. Transactions of the CSAE, 2007; 23(9): 1–6.

Zhong Nan,Luo Xiwen,Qin Qin.Modeling and visualization of three-dimensional soybean root system growth based on growth functions. Transactions of the CSAE, 2008, 24(7): 151–154.

Xu Qijun, Tang Liang, Gu Dongxiang, et al. Architectural parameter-based three dimensional modeling and visualization of rice roots. Transactions of the CSAE, 2010; 26(10): 188–194.

Renatopratademoraes F, Witoldf K. Three-dimensional digital model of a maize plant. Agricultural & Forest Meteorology, 2010; 150(3): 478–488.

Lordan, J., Pascual, M., Fonseca, F., Montilla, V., Papio, J., Rufat, J., et al. An image-based method to study the fruit tree canopy and the pruning biomass production in a peach orchard. HortScience, 2015; 50(12): 1809–1817.

Rufat, J., Villar, J. M., Pascual, M., Falguera, V., & Arbonés, A. Productive and vegetative response to different irrigation and fertilization strategies of an Arbequina olive orchard grown under super-intensive conditions. Agricultural Water Management, 2014; 144, 33–41.

Liu Gang, Zhang Xue, Zong Ze, et al. 3D Reconstruction of Strawberry Based on Depth Information. Transactions of The Chinese Society for Agricultural Machinery, 2017; 48(4): 160–165.

Biskup B, Scharr H, Schurr U, et al. A stereo imaging system for measuring structural parameters of plant canopies. Plant Cell & Environment, 2007, 30(10): 1299–1308.

Ivanov N, Boissard P, Chapron M, et al. Computer stereo plotting for 3-D reconstruction of a maize canopy. Agricultural & Forest Meteorology, 1995, 75(1-3): 85–102.

Su Wei, Guo Hao, Zhao Donglin, et al. Estimation of Actual Leaf Area of Maize Based on Terrestrial Laser Scanning. Transactions of the Chinese Society for Agricultural Machinery, 2016; 47(7): 345–353.

Andújar D, Rueda-Ayala V, Moreno H, et al. Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor. Sensors, 2013; 13(11): 14662–75.

Escolà A, Martínez-Casasnovas J A, Rufat J, et al. Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds. Precision Agriculture, 2017; 18(1): 1–22.

Sun Zhihui, Lu Shenglian, Guo Xinyu, et al. Surfaces reconstruction of plant leaves based on point cloud data. Transactions of the CSAE, 2012; 28(3): 184–190.

Medeiros H, Kim D, Sun J, et al. Modeling Dormant Fruit Trees for Agricultural Automation. Journal of Field Robotics, 2017; 34.

Lowe D G. Objective recognition from local scaleinvariant features. Proc.int.conf.on Comput.vision, 1999, 2.

Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints// International Journal of Computer Vision, 2004; 91–110.

Hu Pengcheng, Guo Yan, Li Baoguo, et al. Three-dimensional reconstruction and its precision evaluation of plant architecture based on multiple view stereo method. Transactions of the CSAE, 2015; 31(11): 209–214.

Aiger D, Mitra N J, Cohenor D. 4-points congruent sets for robust pairwise surface registration. Acm Transactions on Graphics, 2008; 27(3): 1–10.

Zhang Weijie, Liu Gang, Guo Cailin, et al. Apple Tree Leaf Three-dimensional Reconstruction Based on Point Cloud. Transactions of the CSAE, 2017; 48(Supp.): 103–109.

Rusu R B, Marton Z C, Blodow N, et al. Towards 3D Point cloud based object maps for household environments. Robotics & Autonomous Systems, 2008, 56(11):927–941.

Aiger D, Mitra N J, Cohenor D. 4-points congruent sets for robust pairwise surface registration. Acm Transactions on Graphics, 2008; 27(3): 1–10.

Yuan Hua, Pang Jiankeng, Mo Jianwen. Research on Simplification Algorithm of Point Cloud Based on Voxel Grid. Video Engineering, 2015; 39(17): 43–47.

Li Kezhen, Lou Xiaoping, Lv Naiguang. Research on data simplification for point cloud in surface reconstruction. Journal of Beijing Institute of Machinery, 2009; 24(1): 17–20.

Magid E, Soldea O, Rivlin E. A comparison of Gaussian and mean curvature estimation methods on triangular meshes of range image data. Computer Vision & Image Understanding, 2007; 107(3): 139–159.

Liu Rui, Liu Ting, Dong Runru, et al. 3D modeling of maize based on terrestrial LiDAR point cloud data. Journal of China Agricultural University, 2014; 19(3): 196–201.

Anthony P, Xavier S, Scott B, et al. A novel mesh processing based technique for 3D plant analysis. Bmc Plant Biology, 2012; 12(1): 1–13.

GB/T 2443-2011, Steel measuring tares.

Ran N L, Filin S, Eizenberg H. Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points. Precision Agriculture, 2013; 14(6): 586–605.


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