TY - JFULL AU - Mazliham Mohd Su'ud and Pierre Loonis and Idris Abu Seman PY - 2007/2/ TI - Towards Automatic Recognition and Grading of Ganoderma Infection Pattern Using Fuzzy Systems T2 - International Journal of Medical and Health Sciences SP - 1 EP - 7 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/5920 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 1, 2007 N2 - 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. ER -