Work-in-Progress: Prognostics in Arboriculture Using Computer Vision and Statistical Analysis

  • T. K. Sourabh
  • Veena N. HegdeEmail author
  • Nishant Velugula
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 80)


As engineers, we can analyze and mathematically relate the behaviors present in all life sciences. For example, a brain can be modelled as a neural network, or a heart can be modelled as a closed loop control system. Similarly, entities from the world of arboriculture or the plant kingdom as a whole can be modelled and fitted into mathematical models. With the increased use of high speed computers in recent years, process models have a high scope for getting transformed to their digital twins. The performance analysis of such digital twins can be carried out using various techniques and the future behaviour of these systems can be predicted. The non-linear and stochastic behaviour of inputs for these models can be weighed based on the history of their occurrences and the priority level can be assigned to obtain the performance evaluations. This concept has been applied here in this work, to predict the characteristics of trees in terms of their lifespan and strength. The methods of analysis proposed in this paper use widely available open source software platforms, which make the design dedicated, reliable, and accessible to all.


Ree Lifespan Photogrammetry Statistics 


  1. 1.
    Borges Silva, L., Teixeira, A., Alves, M., Elias, R.B., Silva, L.: Tree age determination in the widespread woody plant invader Pittosporum undulatum. 400, 457–467 (15 September 2017) (Elsevier )Google Scholar
  2. 2.
    Chek Mat, M.S., Mohd Nor, M.A., Diah, J.M., Mohd Din, M.A., Hashim, K.A., Manan Samad, A.: Tree age estimation by tree diameter measurement using digital close range photogrammetry (DCPR). In: IEEE Transaction on Control System, Computing and Engineering (ICCSCE), 2014 IEEE International Conference, 28–30 November 2014Google Scholar
  3. 3.
    Lee, H.: Mapping forestation and age of evergreen trees by applying binary coding method to time–series landsat november images. IEEE Trans. Geosci. Remote Sens. 46(11) (November 2008)CrossRefGoogle Scholar
  4. 4.
    Shahbazi, Z., Kaminski, A., Evans, L.: Mechanical stress analysis of tree branches. Am. J. Mech. Eng. 3(2), 32–40 (2015)CrossRefGoogle Scholar
  5. 5.
    al Hagrey, S.A.: Geophysical imaging of root-zone, trunk and moisture heterogeneity. J. Exp. Bot. 58(4), 839–854 (March 2007)Google Scholar
  6. 6.
    Brack, C.: Ph.D. (UBC) Standard point on tree bole for measurement. Forest Measurement and Modelling. Retrieved 18 Apr 2009Google Scholar
  7. 7.
    Mai Husch, B., Miller, C.I., Beers, T.W.: Forest Mensuration, p. 402. Wiley, New York (1982)Google Scholar
  8. 8.
  9. 9.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • T. K. Sourabh
    • 1
  • Veena N. Hegde
    • 2
    Email author
  • Nishant Velugula
    • 3
  1. 1.ModeliCon Infotech LLPBengaluruIndia
  2. 2.B.M.S. College of EngineeringBengaluruIndia
  3. 3.University of Illinois at Urbana-ChampaignChampaignUSA

Personalised recommendations