Optimizasyonlu Bagging Ensemble Yaklaşımıyla Hibrit Özellik Entegrasyonuna Dayalı Ahşap Türü Sınıflandırması

Year 2025, Volume: 26 Issue: 2 , 441 - 455 , 15.10.2025

Kenan Kılıç

https://doi.org/10.17474/artvinofd.1710232

https://izlik.org/JA46CB67UC

Abstract

Türkiye’de ve dünyada yaygın olarak kullanılan meşe (Quercus petrea L.), kestane (Castanea sativa M.) ve sarıçam (Pinus sylvestris L.) ağaç türlerine ait görüntüler bu araştırmada mobil cihaz kullanılarak elde edilmiştir. Çalışmanın temel amacı, bu ahşap türlerini görüntü işleme teknikleri ve istatistiksel sınıflandırma yöntemleri aracılığıyla otomatik ve güvenilir şekilde ayırt ederek ağaç türü tespitini cins bazında gerçekleştirmektir. Bu doğrultuda, görüntülerden HSV (Hue, Saturation, Value), LAB (Lightness, A (green–red), B (blue–yellow)), LBP (Local Binary Pattern) ve Sobel (Sobel Edge Detection Operator) gibi renk ve kenar tabanlı özellikler çıkarılmıştır. Elde edilen bu öznitelikler, Random Forest, XGBoost, CatBoost ve Extra trees algoritmalarıyla değerlendirilerek sınıflandırma başarıları test edilmiştir. Deneysel sonuçlar, özellikle HSV ve LAB gibi renk tabanlı özelliklerin Extra trees algoritmasıyla %97.5 doğruluk sağladığını; tüm özniteliklerin birlikte kullanıldığı, optimizasyon temelli bagging ensemble yaklaşımıyla ise %100 doğruluk elde edilmiştir. Mobil cihazlarla sahada toplanan gerçek dünya verileri üzerinde bu derece yüksek doğrulukların elde edilmesi, önerilen yöntemin pratik uygulamalarda güvenilir bir tür tespit aracı olarak kullanılabileceğini göstermektedir.

Keywords

Yapay zeka , Makine öğrenmesi , Ahşap sınıflandırma , Mobil görüntüleme , Görüntü işleme

Supporting Institution

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Thanks

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Wood Type Classification Based on Hybrid Feature Integration with Optimized Bagging Ensemble Approach

https://izlik.org/JA46CB67UC

Abstract

Images of oak (Quercus petrea L.), chestnut (Castanea sativa M.) and Scots pine (Pinus sylvestris L.) tree species, which are widely used in Türkiye and around the world, were obtained in this study using mobile devices. The primary objective of this study is to automatically and reliably distinguish these wood species using image processing techniques and statistical classification methods, thereby enabling tree species identification at the genus level. In this context, colour and edge-based features such as HSV (Hue, Saturation, Value), LAB (Lightness, A (green–red), B (blue–yellow)), LBP (Local Binary Pattern) and Sobel (Sobel Edge Detection Operator) were extracted from the images. These features were evaluated using Random Forest, XGBoost, CatBoost, and Extra Trees algorithms to test classification performance. The experimental results show that colour-based features such as HSV and LAB achieved 97.5% accuracy with the Extra trees algorithm, while 100% accuracy was achieved with an optimisation-based bagging ensemble approach using all features together. Achieving such high accuracy on real-world data collected in the field using mobile devices demonstrates that the proposed method can be used as a reliable species identification tool in practical applications.

Keywords

Artificial intelligence , Machine learning , Wood classification , Mobile imaging , Image processing

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