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Coconut Tree Structure Analysis - Background Work for an Unmanned Coconut Harvesting Robot Design

  • Sakthiprasad Kuttankulangara ManoharanEmail author
  • Rajesh Kannan MegalingamEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 835)

Abstract

For the designing of an unmanned robotic coconut climber/harvester, we need to model the coconut tree structure especially the tree trunk. The coconut tree trunk is non-deterministic in nature. The variation mainly results in the tree trunk, height, inclination, and treetop. The variation in size/diameter, height and inclination are not same in all coconut trees. In addition to the diameter variation and inclination treetop notches made for harvesting, attack of different organisms, and stem decay due to aging is included for the accuracy. The empirical method is adopted to get some idea about the variation in the coconut tree, in that precisely view the variation of 50 trees in the Kollam district of Kerala, India to reach some conclusion. In this empirical analysis, we tried to include coconut trees with some sort of in variation their structure to make the analysis more effective.

Keywords

Treetop variation Coconut tree trunk Inclination Robot design 

Notes

Acknowledgments

We are grateful to Humanitarian Technology Labs (HuT Labs) of Electronics and Communication department of Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Amritapuri campus, Kollam, India for providing us all the necessary lab facilities and support towards the successful completion of this work.

References

  1. 1.
    Coconut palm stem processing: technical handbook, Produced by Forestry Department: Coconut palm stem processing: a technical handbook. http://www.fao.org/docrep/009/ag335e/AG335E02.htm
  2. 2.
  3. 3.
    Empirical Modeling and Its Applications, Edited by Mamun Habib, InTech, Chapters published July 20, 2016 under CC BY 3.0 license.  https://doi.org/10.5772/61406, https://www.intechopen.com/books/empirical-modeling-and-its-applicationsGoogle Scholar
  4. 4.
    Peter, V., Inorsky, M.: Latitudinal differences in coconut foliar spiral direction: a re- evaluation and hypothesis. Ann. Bot. 82, 133–140 (1998). Department of Biological Sciences, Union College, Schenectady, NY 12308-2311 USACrossRefGoogle Scholar
  5. 5.
    Written by: The Editors of Encyclopædia Britannica https://www.britannica.com/plant/coconut-palm
  6. 6.
    de SousaI, E.F., et al.: Scientia Agricola: Estimating the total leaf area of the green dwarf coconut tree (Cocos nucifera L.). Print version ISSN 0103-9016On-line version ISSN 1678-992X, Sci. Agric. (Piracicaba, Braz.) vol. 62 no. 6 Piracicaba Nov./Dec. (2005)Google Scholar
  7. 7.
    Pahlm, O., Sornmo, L.: Software QRS detection in ambulatory monitoring-A review. Med. Biol. Eng. Comput. 22, 289–297 (1984)CrossRefGoogle Scholar
  8. 8.
    Inorsky, P.V.M.: Foliar spiral direction: a re-evaluation and hypothesis. Ann. Bot. 82, 133–140 (1998). Department of Biological Sciences, Union College, Schenectady, NY 12308-2311 USACrossRefGoogle Scholar
  9. 9.
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
    Megalingam, R.K., Pathmakumar, T., Venugopal, T., Maruthiyodan, G., Philip, A.: DTMF based robotic arm design and control for robotic coconut tree climber. In: IEEE International Conference on Computer, Communication, and Control (IC4-2015)Google Scholar
  15. 15.
    Pramunendar, R.A., Supriyanto, C., Novianto, D.H., Yuwono, I.N., Shidik, G.F., Andono, P.N.: A classification method of coconut wood quality based on gray level co-occurrence matrices. In: International Conference on Robotics, Biomimetics, Intelligent Computational Systems (ROBIONETICS) Yogyakarta, Indonesia, 25–27 November 2013Google Scholar
  16. 16.
    Megalingam, R.K., Sakthiprasad, K.M., Sreekanth, M.M., Vivek, G.V.: A survey on robotic coconut tree climbers – existing methods and techniques. In: International Conference on Advanced Material Technologies (ICAMT)-2017, Dadi Institute of Engineering and Technology, Visakhapatnam, Andhra Pradesh, India, 27–28 December 2016Google Scholar
  17. 17.
    Chattopadhyay, S., Akbar, S.A., Elfiky, N.M., Medeiros, H., Kak, A.: Measuring and modeling apple trees using time-of-flight data for automation of dormant pruning applications. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (2016)Google Scholar
  18. 18.
    Fan, F., Li, J., Gao, G.: Mathematical model application based on statistics in the evaluation analysis of grape wine quality. In: 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (2015)Google Scholar
  19. 19.
    Song, Z., Liu, T., Bai, S.: Modeling based on the effects of grapes for wine. In: 2014 IEEE Workshop on, Electronics, Computer and Applications (2014)Google Scholar
  20. 20.
    Elfiky, N.M., Akbar, S.A., Sun, J.: Automation of dormant pruning in specialty crop production: an adaptive framework for automatic reconstruction and modeling of apple trees. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2015)Google Scholar
  21. 21.
    Xie, K., Yan, F., Sharf, A.: Tree modeling with real tree-parts examples. IEEE Trans. Vis. Comput. Graphics 22(12), 2608–2618 (2016)CrossRefGoogle Scholar
  22. 22.
    Reddy, D., Parthasarathy, V., Rao, S.: Modeling and Analysis of the Effects of Oceanic Wave-Induced Movements of a Boat on the Wireless Link Quality, pp. 1–2 (2017).  https://doi.org/10.1145/3084041.3084073
  23. 23.
    Unnikrishnan, B., Kandasamy, K.: Profiling threat modeling approaches and methodologies for IT and cloud computing. Cent. Cybersecurity Syst. Networks Int. J. Pure Appl. Math. 115(8), 121–126 (2017)Google Scholar
  24. 24.
    Keyser, T.L., Smith, F.W., Lentile, L.B., Shepperd, W.D.: Modeling postfire mortality of ponderosa pine following a mixedseverity wildfire in the black hills: the role of tree morphology and direct fire effects. Forest Sci. 52(5), 530–539 (2006)Google Scholar
  25. 25.
    Tasissa, G., Burkhart, H.E.: An application of mixed effects analysis to modeling thinning effects on stem profile of loblolly pine. Forest Ecol. Manage. 103, 87–101 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringAmrita Vishwa VidyapeethamAmritapuriIndia

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