Username   Password       Forgot your password?  Forgot your username? 


Measuring Surface Area of Leaf based on Multi-Angle Images

Volume 14, Number 9, September 2018, pp. 2153-2162
DOI: 10.23940/ijpe.18.09.p24.21532162

Weizheng Zhang, Weiwei Zhang, Yan Liu, Guoqing Li, and Qiqiang Chen

College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

(Submitted on June 20, 2018; Revised on July 15, 2018; Accepted on August 17, 2018)


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%.


References: 30

                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, and 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,” in Proceedings 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, and Y. Shen, “Leaf Area Measurement based on Image Processing,” in Proceedings 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, and 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, and 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, and N. Ramakant, “Model Validation for Change Detection [machine vision],” In Proceedings 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, and 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, and 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, and 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, and 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” in Proceedings 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


                              Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

                              This site uses encryption for transmitting your passwords.