Odun Yoğunluğu Tahmini için Veri Madenciliği ve Piksel Dağılımı Yaklaşımı

Year 2019, Volume: 21 Issue: 2, 386 - 396, 15.08.2019

Timuçin Bardak , Selahattin Bardak , Eser Sözen

Abstract

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.

Keywords

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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