A feature selection process to select vegetation index which

A variety of research has been done using
remote sensing for oil-palm plantation management.

Tan, Kanniah, & Cracknell (2013) used
remote sensing data to determine the age of oil-palm tree, one of the
significant factor which influences the fruit bunch production. Several
techniques were discussed in this study and it concludes that texture
measurement using Grey-level co-occurrence matrix and fraction of shadow are
the most significant for studying the age of oil-palm trees. 

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Srestasathiern & Rakwatin (2014) used high
resolution satellite images to detect oil-palm plants by computing vegetation
index. Peaks were detected using a 2D semi-variogram technique for the image
texture characterization. The main contribution of this research is the use of feature
selection process to select vegetation index which can best distinguish between
oil-palm and background.  This method
claims a detection accuracy of 90%.

Harnessing the ability of higher spatial
resolution of airborne imagery Shafri et al., (2011) used a multiple step approach to count oil-palm plants from the data
with 1m spatial resolution. This semi-automated approach includes spectral
analysis for discriminating oil-palm and non-oil-palm regions followed by
texture analysis, edge enhancement, image segmentation, morphological analysis
and blob analysis giving an accuracy of 95%. However determining the exact
threshold during the segmentation stage is very crucial to the end results in
this approach.

Some research efforts have been applied for
detecting diseased oil-palms plants. Santoso et al. (2011) used high
resolution Quick bird imagery to map Basel stem rot disease in oil-palm trees.
In this study image segmentation using red band was utilized to discriminate
oil-palm and non-oil-palm zones and then the performance of different
vegetation indices was investigated to differentiate between healthy and
infected oil-palm trees. It concludes that performance of vegetation indices
was acceptable in the late development stage of disease when oil-palm shows
severe symptoms however it was only about 35% in areas with low infection rates
and it varied in different fields depending upon the age of palm and infection
severity.

Zulhaidi et al. (2009) used
airborne hyper spectral data to detect diseased oil-palm plants by
investigating different vegetation indices and red edge techniques. This study
shows that the highest accuracy achieved was 84% using Lagrangian Interpolation
technique (red edge technique) and 80% using Modified simple Ratio (vegetation
index).

Using airborne hyper-spectral imaging system Jusoff (2009) produced
a thematic map of oil-palm plantation in Malaysia. Using the appropriate band
combinations, supervised classification was performed to classify image based
on the reflectance using Spectral Angle Mapper (SAM) algorithm. Ground verification
data showed that the thematic map was able to distinguish between healthy,
stressed and dead plants with an accuracy of 93%.

Early detection of diseased oil-palm plants
using remote sensing image analysis by relying only on spectral data is challenging.
Some of the reasons include that the oil-palm plant canopy does not provide
good spectrum of disease symptoms and it also requires stable sunlight and long
duration to capture significant spectral signatures (Chong, Kanniah, Pohl, & Tan, 2017). Thus
there is need of high spatial accuracy as well which could be achieved by using
airborne sources.