Odun Yoğunluğu Tahmini için Veri Madenciliği ve Piksel Dağılımı Yaklaşımı

Öz

Ahşap ekonomik kalkınmada stratejik bir öneme sahiptir. Yenilikler, tüm endüstrilerde olduğu gibi ahşap endüstrisinde de ticari başarının temelini oluşturur. Ahşabın yoğunluğu, ahşabın fiziksel ve mekanik özellikleri hakkında değerli bilgiler sağlar ve ayrıca orman endüstrisindeki verim ile de doğrudan ilgilidir. Ahşap yapıların fiziksel özelliklerini değerlendirmek için birçok tahribatsız test çalışmaları yapılmıştır. Bu çalışma, gri tonlamalı görüntüdeki piksel sayısı ve veri madenciliğini kullanarak meşe (Quercus robur) ve kayın (Fagus orientalis L.) ağacının yoğunluğunu tahmin etmek için yapıldı. Bu amaçla, ahşap görüntülerden elde edilen piksel yoğunluğu verileri kaydedildi. Bu veriler yapay sinir ağları ve rastgele orman algoritmalarında tanımlayıcı değişkenler olarak kullanılmıştır. Tasarlanan yapay sinir ağı ve rastgele orman algoritmaları, test aşamasında sırasıyla % 95,19 ve          % 96,36 doğrulukla yoğunluk tahmini sağladı. Sonuç olarak, bu çalışma piksel yoğunluğunun ve veri madenciliğinin ahşabın yoğunluğunu öngörmede bir araç olarak kullanılma potansiyeline sahip olduğunu göstermiştir.

Anahtar Kelimeler

• Veri madenciliği,yapay sinir ağları,rastgele orman,dijital görüntüler,odun

Data Mining and Pixel Distribution Approach for Wood Density Prediction

Abstract

Wood has a strategic importance in economic development. Innovations are the basic premise of commercial success in the wood industry, as in all industries. The density of wood provides valuable information about the physical and mechanical properties of the wood, and it is also directly related to the productivity in the forest industry. Many non-destructive test studies have been conducted to evaluate the physical properties of wood structures. This study was conducted to predict the density of wood in oak (Quercus robur) and beech (Fagus orientalis L.) using the number of pixels in grayscale image and data mining. To this purpose, pixel density of data were saved from wood images. This data was used as descriptor variables in artificial neural networks and random forest algorithm. The designed artificial neural network model and random forest algorithm allowed the prediction of density with an accuracy of 95.19% and 96.36%, respectively for the testing phase. As a result, this study showed that pixel density and data mining have the potential to be used as an instrument for predicting the density of wood. 

Keywords

• Data mining,artificial neural networks,random forest,digital images,wood

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