OIL PALM TREE GROWTH MONITORING FOR SMALLHOLDERS BY USING UNMANNED AERIAL VEHICLE

Authors

  • Astina Tugi Tropical Map Research Group, Department of Geoinformation Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
  • Abd Wahid Rasib Tropical Map Research Group, Department of Geoinformation Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
  • Muhammad Akmal Suri Tropical Map Research Group, Department of Geoinformation Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
  • Othman Zainon Tropical Map Research Group, Department of Geoinformation Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
  • Abdul Razak Mohd Yusoff Tropical Map Research Group, Department of Geoinformation Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
  • Mohammad Zulkarnain Abdul Rahman Tropical Map Research Group, Department of Geoinformation Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
  • Nasruddin Abu Sari Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka, 75450 Ayer Keroh, Melaka.
  • Norhadija Darwin Tropical Map Research Group, Department of Geoinformation Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.

DOI:

https://doi.org/10.11113/jt.v77.6855

Keywords:

Smallholder, palm oil, UAV, spectroradiometer, NDVI, MSAVI2

Abstract

The development of the latest technology in agriculture such as using Unmanned Aerial Vehicle (UAV) platform, oil palm tree monitoring can be carried out efficiently by smallholders. Therefore, this study aims to determine the spectral response curve of oil palm tree growth for smallholders by using UAV Platform and payloaded with digital compact camera. The series of UAV images are then to be used to generate an orthophotos image whereby contains two types of spectrum bands which are single spectrum of near Infra-Red (NIR) and three spectrums of visible bands (RGB), respectively. Hence, a spectral response curve graph of oil palm tree condition is able to be produced based on the orthophoto as well as on-site ground validation using handheld spectroradiometer. The growth of the oil palm trees also able to be determined by analyzing the reflectance recorded from the images after generating the Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index 2 (MSAVI2), respectively. This study is successful determined that the low cost UAV platform and digital compact camera able to be used by smallholders in monitoring the oil palm tree growth condition by utilizing remote sensing techniques. As conclusion, this study has showed a good approach for smallholders in determining their oil palm crops condition whereby the results indicate all are identified healthy palm tree after spectral analysis from combination of NIR and RGB UAV images, respectively.  

References

Colomina, I. M. 2014. Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review. ISPRS Journal of Photogrammetry and Remote Sensing. 79-97

Eisenbeiss, H. 2009. UAV Photogrammetry. University of Technology Dresden: Dissertation for the Degree of Doctor of Sciences, Germany.

Gyanesh Chander, B. L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+,and EO-1 ALI sensors. Remote Sensing of Environment.

Jensen, J. 1996. Introductory DigitalImage Processing : A Remote Sensing Perspective. 2nd Edition. Englewood Cliffs, New Jersey: Prentice Hall.

Kumar, R. L. 1973. Light Ray tracing through a leaf cross-section. 2950-2954

Lucieer, A., 2012. Using A Micro UAV For Ultra-high Resolution Multi-sensor Observation of Antartic Moss Beds. International Archives of the Phototgrammetry. Remote Sensing and Spatial Information Sciences. XXXIX-B1: 429-433

MySpatialBlog. 2014. UAV: The Future of Precision Agriculture, Retrieve May 3, 2015 from myspatial.com.my/index.php/uav-future-precision-agriculture.

Salami, E., Barrado, C., & Pastor, E. 2014. UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas. Remote Sensing ISSN 2072-4292.

Sunderajoo, G. A. 2014. Coastal Aerial Mapping using Micro Unmanned Aerial Vehicle.

Vauhkonen, J. L. 2012. Comparative Testing of Single-Tree Detection Algorithms Under Different Types of Forest. Forestry. 27-40

Xie, Y., Sha, Z., & Yu, M. 2008. Remote Sensing Imagery In Vegetation Mapping. Journal of Plant Ecology

Downloads

Published

2015-12-20

How to Cite

OIL PALM TREE GROWTH MONITORING FOR SMALLHOLDERS BY USING UNMANNED AERIAL VEHICLE. (2015). Jurnal Teknologi, 77(26). https://doi.org/10.11113/jt.v77.6855