Prediction of wood density by using red- green-blue (rgb) color and fuzzy logic techniques

Ahşap malzemenin yoğunluğu ahşabın mekaniksel özelliklerini etkilemesinden dolayı önemlidir. Mevcut yapay zeka teknikleri arasında bulanık mantık tahminlerde iyi bir yöntem olarak ortaya çıkmaktadır. Dijital görüntü tekniği bir görüntüden anlamlı bir bilgi elde etmek için kullanılan güçlü bir yöntemdir. Test örneklerinin yoğunlukları ölçülmüştür. Ayrıca, örneklerin renk yoğunluğunu ölçmek için Kırmızı-Yeşil-Mavi (KYM) renk muayenesine dayanan dijital görüntü analizi uygulanmıştır. Ölçülen değerler ve ahşabın KYM renk yoğunluğu temelinde yeni bir bulanık mantık modeli geliştirilmiştir. Sonrasında deneyler ve model verileri karşılaştırılmıştır. Hazırlanan modelin çıkarımları ile deneysel veriler %98.17 oranında doğruluk göstermiştir. Sonuç olarak, bulanık mantık odun yoğunluğu tahmini için geçerli bir yöntem olduğu tespit edilmiştir.

Kırmızı-yeşil-mavi (kym) renk ve bulanık mantık teknikleri kullanılarak odun yoğunluğu tahmini

Density 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.

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