Determination of Green Leaves Density Using Normalized Difference Vegetation Index via Image Processing of In-Field Drone-Captured Image
Keywords:
Normalized Difference Vegetation Index (NDVI), Unmanned Aerial Vehicle (UAV), Image Processing, Remote Sensing,Abstract
Normalized Difference Vegetation Index (NDVI) is a technique which utilizes the near-infrared and visible bands of the electromagnetic spectrum in order to quantify the vegetation density in a specific area. This study presents a method to determine the NDVI levels of a certain rice paddy through the use of images captured using unmanned aerial vehicle (UAV) and a camera system. The camera system is developed from two action cameras, one with its infrared filter removed and replaced with blue notch filter. It is then attached to a UAV for capturing aerial images of a certain field. The images were then processed in a program written in MATLAB®. A total of 30 samples were selected in a rice field. Each sample is a 1x1-meter area. The NDVI values of the samples were first measured using Oklahoma State University (OSU) Greenseeker prototype, then the images of these samples were taken using the camera system developed. The images were then processed to get the NDVI values. Overall, the measurement of the camera system showed good consistency. The F-test conducted also implied that the system is reliable and can be used as an alternate in determining the NDVI levels in the field.References
Balisacan, A. M., & Sebastian, L. S. (2006). Securing Rice, Reducing Poverty: Challenges and Policy Directions. Retrieved from http://lynchlibrary.pssc.org.ph:8081/handle/0/1062.
Khosla, R. (2010, August). Precision agriculture: challenges and opportunities in a flat world. In 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane, Australia.
Dobermann, A., &Cassman, K. G. (1996). Precision Nutrient Management in 20 Intensive Irrigated Rice Systems – The Need for Another Revolution On-Farm (Asia). Better Crops International, 10(2).
Orillo, J.W et al (2014). Rice Plant Nitrogen Level Assessment through Image Processing using Artificial Neural Network. Retrieved from http://ieeexplore.ieee.org/.
Orillo, J.W et al (2014). Identification of Diseases in Rice Plant (Oryza Sativa) using Back Propagation Artificial Neural Network. Retrieved from http://ieeexplore.ieee.org/
Orillo, J.W et al (2016). Rice Plant Disease Identification and Detection Technology through Classification of Microorganisms using Fuzzy Neural Network. 72:2 (2015) 1 6, www.jurnalteknologi.utm.my, eISSN 2180–3722.
Holme, A.McR., Burnside, D.G. & Mitchell, A.A. (1987). The development of a system for monitoring trend in range condition in the arid shrublands of Western Australia.Australian Rangeland Journal 9:14-20.
Rouse, J. W., Haas R. H., Schell J. A., &Deering D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA
Huang, J., Wang, H., Dai, Q., & Han, D. (n.d.). Analysis of NDVI Data for Crop Identification and Yield Estimation. Nanjing, China: State Key Lab. of Hydrol.-Water Resources & Hydraulic Eng., Hohai Univ. Retrieved from http://ieeexplore.ieee.org/
Shah, P. (2014). Image Processing Aerial Thermal Images to Determine Water Stress on Crops. Retrieved from http://web.stanford.edu/class/ee368/Project_Autumn_1314/.
Berni, J. A., Zarco-Tejada, P. J., Suárez, L., &Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. Geoscience and Remote Sensing, IEEE Transactions on, 47(3), 722-738.
Vedaldi, A., and B. Fulkerson. "VLFeat: An Open and Portable Library of Computer Vision Algorithms." Retrieved from http://www.vlfeat.org/.
Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.