Towards Automatic Recognition and Grading of Ganoderma Infection Pattern Using Fuzzy Systems
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061936Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1679
 Aydin. Fuzzy set approaches to classification of rock masses. Engineering Geology, (74):227-245, 2004.
 Nathalie Colin and Michel Moruzzis. Radar target recognition by fuzzylogic. IEEE National Radar Conference, pages 257-262, 1997.
 Gonzalez and Woods. Degital Image Processing (2nd Edition). Addison-Wesley Publishing Company, 2002.
 AS Idris, D Ariffin, TR Swinburne, and TA Watt. The identity of ganoderma species responsible for bsr disease of palm oil in malaysia- morphological characteristics. MPOB Information, Series TT No 77a,2000.
 AS Idris, D Ariffin, TR Swinburne, and TA Watt. The identity of ganoderma species responsible for bsr disease of palm oil in malaysia-pathogenicity test. MPOB Information, Series TT No. 77b, 2000.
 AS Idris, M Yamaoka, S Hayakawa, MW Basri, I Noorhashimah, andD Ariffin. Pcr technique for detection of ganoderma, mpob information. MPOB Information, Series TT No 188, 2003.
 Alexandra P. Jacquin and Asaad Y. Shamseldin. Development of rainfall-runoff models using takagi-sugeno fuzzy inference systems. Journal ofHydrology, 329(1-2), 2006.
 Negnevitsk Michael. Artificial Intelligence : A Guide to Intelligent Systems. Pearson Education Limited, 2005.
 Nelson. Automatic vehicle detection in infrared imagery using a fuzzy inference - based classification system. IEEE Transaction on Fuzzy Systems, 9(1), February 2001.
 G Nurcahyo, SM Shamsuddin, RA Alias, and MN Md. Sap. Selection of defuzzification method to obtain crisp value for representing uncertain data in a modified sweep algorithm. Journal of Computer Science and Technology, 3(2):22-28, 2003.
 Turner PD. Palm oil Diseases and Disorers. Oxford University Press,1981.
 Amin Sanket, Carl Byington, and Matthew Watson. Fuzzy inference and fusion for health state diagnosis of hydraulic pumps and motors. Annual Conference of the North American Fuzzy Information Processing Society- NAFIPS, pages 13-18, 2005.
 Schwarze and Fermer. Ganoderma on tree - differentiation of species and studies of invasiveness. available online www.enspec.com/articles/.
 Idris Abu Seman and Ariffin Darus. Ganoderma peny akit reput pangkal batang dan kawalannya. Risalah Sawit, No 11, 2003.