Determination of Green Leaves Density Using Normalized Difference Vegetation Index via Image Processing of In-Field Drone-Captured Image

John William Orillo, Gaudencio Bansil Jr, John Joseph Bernardo, Coleen Dizon, Helen Imperial, Anna Mae Macabenta, Robert Palima Jr


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.


Normalized Difference Vegetation Index (NDVI); Unmanned Aerial Vehicle (UAV); Image Processing; Remote Sensing;

Full Text:



Balisacan, A. M., & Sebastian, L. S. (2006). Securing Rice, Reducing Poverty: Challenges and Policy Directions. Retrieved from

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

Orillo, J.W et al (2014). Identification of Diseases in Rice Plant (Oryza Sativa) using Back Propagation Artificial Neural Network. Retrieved from

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,, 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

Shah, P. (2014). Image Processing Aerial Thermal Images to Determine Water Stress on Crops. Retrieved from

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


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131