is an important wood property since it correlates to mechanical
properties of wood. Fuzzy logic, among the various available Artificial
Intelligence techniques, emerges as a good technique in predicting. Digital
image analysis is an powerful tool to obtain meaningful data out of an image. In this study, digital image processing based on a
red–green–blue (RGB) color examination was practiced to measure the intensity
of wood color. Densities of the test samples were measured. Then, a new fuzzy
logic model was developed based on these measured values and RGB color
intensity of wood. Afterwards, the
experimental and modeling data results were compared. 98.17% accuracy was observed between the
measurement and the fuzzy logic model. Consequently, Fuzzy logic is visable
method for the prediction of the wood density.
1. Ors Y. and Keskin H., “Wood materials science (Ağaç malzeme teknolojisi)”, Gazi University Publication, No: 2001-352, Ankara, (2008).
2. Zareiforoush H., Minaei S., Alizadeh M.R. and Banakar A., “A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice”, Measurement, 66: 26-34, (2016).
3. Chen C.L. and C.L. Tai C.L., “Adaptive fuzzy color segmentation with neural network for road detections”, Engineering Applications Artificial Intelligence, 23 (3): 400-410, (2010).
4. Hassan M.A., Yusof Y., Azmi M.A. and Mazli M.N., “Fuzzy Logic Based Intelligent Control of RGB Colour Classification System for Undergraduate Artificial Intelligence Laboratory”, The World Congress on Engineering, London, 713-718, (2012).
5. Tou J.Y., Lau P.Y. and Tay Y.H., “Computer vision–based wood recognition system”, International Workshop on Advanced Image Technology (IWAIT), Bangkok, Thailand, 197–202, (2007).
6. Esteban L.G., Fernández F.G., Palacios P. and Conde M., “Artificial neural networks in variable process control: application in particleboard manufacture”, Investıgacıon Agrarıa-Sıstemas Y Recursos Forestales, 18(1): 92–100, (2009).
7. Khalid M., Lee E.L.Y., Yusof R. and Nadaraj m., “Design of an intelligent wood species recognition system”, International Journal of Simulation: Systems, Science and Technology, 9(3): 9–19, (2008).
8. Ozsahin S., “Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis”, European Journal of Wood and Wood Products, 71(6): 769-777, (2013).
9. Ruz G.A., Estévez P.A., and Perez C.A., “A neurofuzzy color image segmentation method for wood surface defect detection”, Forest Products Journal, 55(4): 52-58, (2005).
10. Cavdarlı M. and Seke E., “Measuring roughness on wood surfaces for detection of defects using multi-frame imaging”, The International Symposium on Innovations in Intelligent Systems and Applications, Kayseri, 21-23, (2010).
11. Cheung W.W.L., Pitcher T.J. and Pauly D., “A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing”, Biological Conservation, 124(1): 97-111, (2005).
12. Uraon K.K. and Kumar S., “Analysis of Defuzzification Method for Rainfall Event”, International Journal of Computer Science and Mobile Computing, 5(1): 341–354, (2016).
13. URL1,https://en.wikipedia.org/wiki/Fuzzy_set_ operations#Fuzzy_unions. (Accessed 9 Semtember 2016).
14. URL2,http://www.massey.ac.nz/~nhreyes/ MASSEY/159741/Lectures/Lec2012-3-159741-FuzzyLogic-v.2.pdf. (Accessed 9 September 2016).
15. Bardak S., Tiryaki S., Nemli G. and Aydın A., “Investigation and neural network prediction of wood bonding quality based on pressing conditions”, International Journal of Adhesion and Adhesives, 68: 115-123, (2016).
16. Tiryaki S. and Hamzacebi C., “Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks”, Measurement, 49: 266-274, (2014).
17. Tiryaki S., Bardak S. and Bardak T., “Experimental investigation and prediction of bonding strength of Oriental beech (Fagus orientalis Lipsky) bonded with polyvinyl acetate adhesive”, Journal of Adhesion Science and Technology, 29(23): 2521-2536, (2015).
18. ASTM D1666 – 87, “Standard Methods for conducting Machining Tests of Wood and Wood-Base Materials”, (2004).
19. TS 2472, “Wood Determination of density for physical and mechanical tests”, (2005).
20. Nopens I., Foubert I., Graef V.D., Laere D.V. Dewettinck K. And Vanrolleghem P., “Automated image analysis tool for migration fat bloom evaluation of chocolate coated food products”, Journal of Food Science and Technology, 4:1884–1891, (2008).
21. Nian C.Y., Chuang S.F. and Tarng Y.S., “A new algorithm for a three-axis auto-alignment system using vision inspection”, Journal of Materials Processing Technology, 171: 319–329, (2006).
22. Lv B., Li B., Chen S., Chen J. and Zhu B., “Comparison of color techniques to measure the color of parboiled rice”, Journal of Cereal Science, 50(2): 262-265, (2009).
23. Thalmann C., Freise J., Heitland W. and Bacher S., “Effects of defoliation by horse chestnut leafminer (Cameraria ohridella) on reproduction in Aesculus hippocastanum”, Trees, 17: 383–388, (2003).
24. Akkurt, S., Tayfur, G. and Can, S., “Fuzzy logic model for the prediction of cement compressive strength” Cement and Concrete Research, 34(8):1429-1433, (2004).
25. Ozcifci, A., Yapici, F. and Altun, S., “The prediction of effect of grain angle over modulus of rupture and modulus of elasticity values on Scotch pine with Fuzzy logic classifier” 5th International Advanced Technologies Symposium (IATS’09), Karabuk, 13-15, (2009).