Image processing analysis of geospatial uav orthophotos for palm oil plantation monitoring

Unmanned Aerial Vehicle (UAV) is one of the tools that can be used to monitor palm oil plantation remotely. With the geospatial orthophotos, it is possible to identify which part of the plantation land is fertile for planted crops, means to grow perfectly.

Authors: F Fahmi, D Trianda, U Andayani and B Siregar
 
Published under licence by IOP Publishing Ltd
Journal of Physics: Conference Series, Volume 978, conference 1
 
Abstract
 
Unmanned Aerial Vehicle (UAV) is one of the tools that can be used to monitor palm oil plantation remotely. With the geospatial orthophotos, it is possible to identify which part of the plantation land is fertile for planted crops, means to grow perfectly. It is also possible furthermore to identify less fertile in terms of growth but not perfect, and also part of plantation field that is not growing at all. This information can be easily known quickly with the use of UAV photos. In this study, we utilized image processing algorithm to process the orthophotos for more accurate and faster analysis. The resulting orthophotos image were processed using Matlab including classification of fertile, infertile, and dead palm oil plants by using Gray Level Co-Occurrence Matrix (GLCM) method. The GLCM method was developed based on four direction parameters with specific degrees 0°, 45°, 90°, and 135°. From the results of research conducted with 30 image samples, it was found that the accuracy of the system can be reached by using the features extracted from the matrix as parameters Contras, Correlation, Energy, and Homogeneity.
 
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
 

Illustration Photo: Aerial footage of palm oil and the forest in Sentabai Village, West Kalimantan, Indonesia (credits: Nanang Sujana / CIFOR / Flickr Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))

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