Abstract: Most of the oil palm plantations have been threatened
by Basal Stem Rot (BSR) disease which causes serious economic
impact. This study was conducted to identify the healthy and BSRinfected
oil palm tree using thirteen color indices. Multispectral and
thermal camera was used to capture 216 images of the leaves taken
from frond number 1, 9 and 17. Indices of normalized difference
vegetation index (NDVI), red (R), green (G), blue (B), near infrared
(NIR), green – blue (GB), green/blue (G/B), green – red (GR),
green/red (G/R), hue (H), saturation (S), intensity (I) and thermal
index (T) were used. From this study, it can be concluded that G
index taken from frond number 9 is the best index to differentiate
between the healthy and BSR-infected oil palm trees. It not only gave
high value of correlation coefficient (R=-0.962), but also high value
of separation between healthy and BSR-infected oil palm tree.
Furthermore, power and S model developed using G index gave the
highest R2 value which is 0.985.
Abstract: This paper deals with the extraction of information from the experts to automatically identify and recognize Ganoderma infection in oil palm stem using tomography images. Expert-s knowledge are used as rules in a Fuzzy Inference Systems to classify each individual patterns observed in he tomography image. The classification is done by defining membership functions which assigned a set of three possible hypotheses : Ganoderma infection (G), non Ganoderma infection (N) or intact stem tissue (I) to every abnormalities pattern found in the tomography image. A complete comparison between Mamdani and Sugeno style,triangular, trapezoids and mixed triangular-trapezoids membership functions and different methods of aggregation and defuzzification is also presented and analyzed to select suitable Fuzzy Inference System methods to perform the above mentioned task. The results showed that seven out of 30 initial possible combination of available Fuzzy Inference methods in MATLAB Fuzzy Toolbox were observed giving result close to the experts estimation.