UAV based precision agriculture using HIPI

Authors

  • Chemwotie Kipkurui Brian  Department of Computing, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Keywords:

UAV, HIPI, Image

Abstract

Over a number of years, there has been great movement towards mechanizing agriculture. This has not left out the use of unmanned aircraft Vehicles (UAVs). There has been models created to identify and suggest applicable solutions on the problems identified using the models based on UAVs. Over time, the need for real time identification of problems and their respective solutions has risen. This paper looks into the use of Hadoop Image Processing Interface (HIPI) in development of a model that can be used to provide real time solutions to the farmer based on the images gathered by the UAV on the agricultural fields. HIPI was developed to simplify the parallelization of image processing using Hadoop MapReduce framework.

References

  1. Ruben D'Sa, Devon Jenson, Nikolaos Papanikolopoulos, "SUAV:Q - a hybrid approach to solar-powered flight", Robotics and Automation (ICRA) 2016 IEEE International Conference on, pp. 3288-3294, 2016.
  2. S. Fouché and N. W. Booysen,"Assessment of crop stress conditions using low altitude aerial color-infrared photography and computer image processing, "Geocarto Int., vol. 2, pp. 25–31, 1994.
  3. Y. Inoue, S. Morinaga, and A. Tomita, "A blimp-based remote sensing system for low ltitude monitoring of plant variables: A preliminary experiment for agricultural and ecological applications," Int. J. Remote Sens. , vol. 21, pp. 379 – 385, 2000.
  4. E. R. Hunt, Jr., C. S. T. Daughtry, C. L. Walthall, J. E. McMurtrey III, andW.P.Dulaney, " Agricultural remote sensing using radio-controlled model aircraft,"in Digital Imaging and Spectral Techniques: Applications to Precision Agriculture and Crop Physiology, ASA Special Publication 66. Madison, WI, USA: American Society of Agronomy,2003, pp. 197 –205.
  5. S. R. Herwitz, L. F. Johnson, S. E. Dunagan, R. G. Higgins, D. V. Sullivan, J. Zheng, B. M. Lobitz, J. G. Leung, B. A. Gallmeyer, M. Aoyogi, R. E. Slye, and J. A. Brass, "Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support,"Comput. Electron. Agric. ,vol. 44, pp. 49–61, 2004
  6. J. Torres-Sánchez, F. López-Granados, J.M. Peña, "An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops," Computers and Electronics in Agriculture, 114, 43-52, 2015.
  7. F.A. Vega, F.C. Ramírez, M.P. Saiz, F.O. Rosúa, "Multitemporal imaging using an unmanned aerial
  8. vehicle for monitoring a sunflower crop," Biosystems Engineering, 132, 19-27, 2015.
  9. Sugiura, R., Noguchi, N., & Ishii, K. Remote -sensing technology for vegetation monitoring using an unmanned helicopter. Biosystems Engineering, 90(4), 369-379, 2005
  10. Jannoura, R., Brinkmann, K., Uteau, D., Bruns, C., & Joergensen, R. G. Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter. Biosystems engineering , 129, 341-351, 2015
  11. Sweeney C, Liu L, Arietta S, Lawrence J. HIPI: a Hadoop image processing interface for image-based mapreduce tasks. Chris. University of Virginia. 2011.
  12. Zermas, D., Teng, D., Stanitsas, P., Bazakos, M., Kaiser, D., Morellas, V., Mulla, D. & Papanikolopoulos, N. (2015). Automation solutions for the evaluation of plant health in corn fields.. IROS (p./pp. 6521-6527), : IEEE. ISBN: 978-1-4799-9994-1
  13. A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: A review," ACM Comput. Surv., vol. 31, no. 3, pp. 264–323, Sep. 1999. [Online]. Available: http://doi.acm.org/10.1145/331499.331504
  14. Hanif, M., & Ali, U. (2006, July). Optimized visual and thermal image fusion for efficient face recognition. In 2006 9th International Conference on Information Fusion (pp. 1-6). IEEE.
  15. Rouse JW, Haas RH, Schell JA, Deering DW, Harlan JC (1974) Monitoring the vernal advancements and retrogradation of natural vegetation. In: MD UG, editor. NASA/GSFS final report. p. 371.
  16. Gamon, J.; Penuelas, J.; Field, C. (1992) A narrow-waveband spectral index that tracks diurnal changes in potosynthetic efficiency. Remote Sensing of Environment, 41, 35-44.
  17. R. Telea and J. J. V. Wijk, "An augmented fast marching method for computing skeletons and centerlines," in in Proc. of the Symposium on Data Visualisation (VisSym’02), 2002, pp. 251–259.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2017-04-30

Issue

Volume 2, Issue 2, March-April-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Chemwotie Kipkurui Brian, " UAV based precision agriculture using HIPI, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.766-770, March-April-2017.