Measuring Surface Area of Leaf based on Multi-Angle Images

doi:10.23940/ijpe.18.09.p24.21532162

Abstract

Abstract: The measurement of plant leaf area (LA) has important guiding significance for the diagnosis of plant growth status. Most of the existing methods for measuring LA are contact measurement. This paper proposes a method to directly create a 3D model of the leaf and calculate the surface area of the leaf in the natural state. Firstly, the digital camera is calibrated to obtain the camera parameters. Then, the leaves are photographed from multi-angles in order to obtain the three-dimensional point cloud; the images are processed by Photomodeler. Use MATLAB programming to achieve 3D modeling of the leaf and calculate the surface area using scanner combination Photoshop software methods. The experimental results show that the method proposed has a prominent effect on the measurement of the leaf under natural conditions with an accuracy of 99%.

Key words: camera calibration, PhotoModeler, point cloud, 3D modeling, surface area

Weizheng Zhang, Weiwei Zhang, Yan Liu, Guoqing Li, and Qiqiang Chen. Measuring Surface Area of Leaf based on Multi-Angle Images [J]. Int J Performability Eng, 2018, 14(9): 2153-2162.

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References

[1] Y. Rouphael, C. M. Rivera, M. Cardarelli, et al., “Leaf Area Estimation from Linear Measurements in Zucchini Plants of Different Ages,” Journal of Pomology and Horticultural Science, Vol. 81, No. 2, pp. 238-241, 2006
[2] D. C. Camargo, F. Montoya, M. A. Moreno, et al., “Impact of Water Deficit on Light Interception, Radiation Use Efficiency and Leaf Area Index in a Potato Crop (Solanum tuberosum L.),” Journal of Agricultural Science, Vol. 154, No. 4, pp. 662-673, 2016
[3] H. L. Yu, P. Huang, H. H. Su, et al., “Synchronous Measurement of 3D Morphology and Illuminance of Plant Leaves based on Binocular Vision,” Transactions of the Chinese Society of Agricultural Engineering, Vol. 32, No. 10, pp. 149-156, 2016
[4] J. N. Cobb, G. DeClerck, A. Greenberg, et al., “Next-generation Phenotyping: Requirements and Strategies for Enhancing Our Understanding of Genotype-phenotype Relationships and its Relevance to Crop Improvement,” Theoretical and Applied Genetics, Vol. 126, No. 4, pp. 867-887, 2013
[5] G. Fascella, S. Darwich,Y. Rouphael, “Validation of a Leaf Area Prediction Model Proposed for Rose,” Chilean Journal of Agricultural Research, Vol. 73, No. 73, pp. 73-76, 2013
[6] Y. Rouphael, A. H. Mouneimne, A. Ismail, et al., “Modeling Individual Leaf Area of Rose (Rosa hybrida L.) based on Leaf Length and Width Measurement,” Photosynthetica, Vol. 48, No. 1, pp. 9-15, 2010
[7] A. P. Gong, X. Wu, Z. J. Qiu, et al., “A Handheld Device for Leaf Area Measurement,” Computers and Electronics in Agriculture, Vol. 98, No. 7, pp. 74-80, 2013
[8] B. Chen, Z. Fu, Y. Pan, et al., “Single Leaf Area Measurement Using Digital Camera Image,” inProceedings of International Conference on Computer and Computing Technologies in Agriculture, pp. 525-530, Springer Berlin Heidelberg, 2010
[9] B. Valle, T. Simonneau, R. Boulord, et al., “PYM: A New, Affordable, Image-based Method Using a Raspberry Pi to Phenotype Plant Leaf Area in a Wide Diversity of Environments,” Plant Methods, Vol. 13, No. 1, pp. 98, 2017
[10] H. Ren, Y. Zhang,Y. Shen, “Leaf Area Measurement based on Image Processing,” inProceedings of International Conference on Measuring Technology and Mechatronics Automation, pp. 580-582, IEEE Computer Society, 2010
[11] F. Emondino and S. Campana. “Fast and Detailed Digital Documentation of Archaeological Excavations and Heritage Artifacts,”Computer Applications and Quantitative Methods in Archaeology, pp. 36-42, 2007
[12] F. Poux, R. Neuville, W. L. Van,G. A. Nys, “3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects,” Geosciences, Vol. 7, No. 4, pp. 96, 2017
[13] T. Luhmann, “Close Range Photogrammetry for Industrial Applications,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, No. 6, pp. 558-569, 2010
[14] D. Gorpas, K. Politopoulos,D. Yova, “A Binocular Machine Vision System for Three-Dimensional Surface Measurement of Small Objects,” Computerized Medical Imaging and Graphics, Vol. 31, No. 8, pp. 625-637, 2007
[15] M. Bejanin, A. Huertas, G. Medioni,N. Ramakant, “Model Validation for Change Detection [machine vision],” InProceedings of the Second IEEE Workshop on Applications of Computer Vision, pp. 160-167, 1994
[16] Z. Jia, B. Wang, W. Liu, et al., “An Improved Image Acquiring Method for Machine Vision Measurement of Hot Formed Parts,” Journal of Materials Processing Technology, Vol. 210, No. 2, pp. 267-271, 2010
[17] E. S. Gadelmawla, “Computer Vision Algorithms for Measurement and Inspection of Spur Gears,” Measurement, Vol. 44, No. 9, pp. 1669-1678, 2011
[18] C. Igathinathane, U. Ulusoy,L. O. Pordesimo, “Comparison of Particle Size Distribution of Celestite Mineral by Machine Vision ΣVolume Approach and Mechanical Sieving,”Powder Technology, Vol. 215, pp. 137-146, 2012
[19] T. Luhmann, C. Fraser,H. G. Maas, “Sensor Modelling and Camera Calibration for Close-range Photogrammetry,”ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 115, pp. 37-46, 2016
[20] M. Scaioni, T. Feng, L. Barazzetti, et al., “Image-based Deformation Measurement,” Applied Geomatics, Vol. 7, No. 2, pp. 75-90, 2015
[21] G. Armin and S. Thomas, “Calibration and Orientation of Cameras in Computer Vision,” Springer Verlag, Berlin, 2005
[22] T. Markowski, “Application of the Photomodeler Software and Matlab Environment for Analysis of Objects Movement Parameters based on Image Sequences,” Archives of Photogrammetry, Cartography and Remote Sensing, special issue “Measurement Technologies in Surveying”, Vol. 23, pp. 85-96, 2013
[23] C. G. Wang, “Gridding and Visualization Technology for the Scientific Computing,” Science Press, Beijing, 2011
[24] S. H. Liu, Q. S. Luo,W. Huang, “Improved Delauney Triangulation Method for Generating 2 Dimensional Network Structure,” Journal of Wuhan University (Engineering Science), Vol. 38, No. 6, pp. 1-5, 2005
[25] S. W. Cheng, T. K. Dey,J. Shewchuk, “Delaunay Mesh Generation,” Imprint Chapman and Hall, New York, 2012
[26] K. E.Brassel and D. Reif, “Procedure to Generate TIN Polygons,”Geographical Analysis, Vol. 11, pp. 1979
[27] M. J.McCullagh and C. G. Ross, “Delaunay Triangulation of a Random Data Set for Isarithmic Mapping,”The Cartographic Journal, Vol. 17, pp. 93-99, 1980
[28] D. Y.Lee and S. S. Lam, “Protocol Design for Dynamic Delaunay Triangulation” inProceedings of International Conference on Distributed Computing Systems, pp. 26-26, IEEE, 2007
[29] B. Žalik, “An Efficient Sweep-line Delaunay Triangulation Algorithm,” Computer-Aided Design, Vol. 37, No. 10, pp. 1027-1038, 2005
[30] J. J.Chen and Y. Zheng, “Redesign of a Conformal Boundary Recovery Algorithm for 3D Delaunay Triangulation,” Journal of Zhejiang University-Science A, Vol. 7, No. 12, pp. 2031-2042, 2006