Optimizasyonlu Bagging Ensemble Yaklaşımıyla Hibrit Özellik Entegrasyonuna Dayalı Ahşap Türü Sınıflandırması

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

1. Breiman L (2001) Random forests. Machine Learning, 45:5-32. https://doi.org/10.1023/A:1010933404324
2. Chen C, Kuang Y, Zhu S, Burgert I, Keplinger T, Gong A, Hu L (2020) Structure–property–function relationships of natural and engineered wood. Nature Reviews Materials, 5(9):642-666. https://doi.org/10.1038/s41578-020-0195-z
3. Chen T, Guestrin C (2016) XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 785-794. https://doi.org/10.1145/2939672.2939785
4. Feng Y, Hao H, Lu H, Chow CL, Lau D (2024) Exploring the development and applications of sustainable natural fiber composites: a review from a nanoscale perspective. Composites Part B: Engineering, 111369. https://doi.org/10.1016/j.compositesb.2024.111369
5. Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Machine Learning, 63:3-42. https://doi.org/10.1007/s10994-006-6226-1
6. Hu S, Li K, Bao X (2015) Wood Species Recognition Based on SIFT Keypoint Histogram. In: 2015 8th International Congress on Image and Signal Processing (CISP), pp 702-706. IEEE. https://doi.org/10.1109/CISP.2015.7407968
7. Humeau Heurtier A (2019) Texture feature extraction methods: a survey. IEEE Access, 7:8975-9000. https://doi.org/10.1109/ACCESS.2018.2890743
8. Hwang SW, Sugiyama J (2021) Computer vision-based wood identification and its expansion and contribution potentials in wood science: a review. Plant Methods, 17(1):47. https://doi.org/10.1186/s13007-021-00746-1

9. Ibrahim I, Khairuddin ASM, Abu Talip MS, Arof H, Yusof R (2017) Tree species recognition system based on macroscopic image analysis. Wood Science and Technology, 51:431-444. https://doi.org/10.1007/s00226-016-0859-4
10. Khalid M, Lee ELY, Yusof R, Nadaraj M (2008) Design of an intelligent wood species recognition system. International Journal of Simulation System, Science and Technology, 9(3):9-19. https://doi.org/10.1109/ICA.2011.6130117
11. Khalid M, Yusof R, Khairuddin ASM (2011) Tropical Wood Species Recognition System Based on Multi-Feature Extractors and Classifiers. In: 2011 2nd International Conference on Instrumentation Control and Automation, pp 6-11. IEEE. https://doi.org/10.1109/ICA.2011.6130117
12. Kluyver T, Ragan-Kelley B, Pérez F, Granger B, Bussonnier M, Frederic J, Kelley K, Hamrick J, Grout J, Corlay S, Ivanov P, Avila D, Abdalla S, Willing C (2016) Jupyter Notebooks-A Publishing Format for Reproducible Computational Workflows. In: Loizides F, Schmidt B (eds) Positioning and power in academic publishing: Players, agents and agendas, pp 87-90. IOS Press. https://doi.org/10.3233/978-1-61499-649-1-87
13. Kour A, Yadav VK, Maheshwari V, Prashar D (2013) A review on image processing. International Journal of Electronics Communication and Computer Engineering, 4(1):270-275.
14. Margulis D (2005) Photoshop LAB Color: The Canyon Conundrum and Other Adventures in the Most Powerful Colorspace. Peachpit Press.
15. Martins J, Oliveira LS, Nisgoski S, Sabourin R (2013) A database for automatic classification of forest species. Machine Vision and Applications, 24:567-578. https://doi.org/10.1007/s00138-012-0417-5
16. Mutlag WK, Ali SK, Aydam ZM, Taher BH (2020) Feature extraction methods: a review. Journal of Physics: Conference Series, 1591(1):012028. https://doi.org/10.1088/1742-6596/1591/1/012028
17. Nasir V, Nourian S, Avramidis S, Cool J (2019) Classification of thermally treated wood using machine learning techniques. Wood Science and Technology, 53:275-288. https://doi.org/10.1007/s00226-018-1073-3
18. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971-987.https://doi.org/10.1109/TPAMI.2002.1017623
19. Pratondo A, Ong SH, Chui CK (2014) Region Growing for Medical Image Segmentation Using a Modified Multiple-Seed Approach on a Multi-Core CPU Computer. In: The 15th International Conference on Biomedical Engineering: ICBME 2013, Singapore, pp 112-115. Springer. https://doi.org/10.1007/978-3-319-02913-9_29
20. Pratondo A, Roedavan R, Sujana AP, Rizqyawan MI (2020) Medical image Segmentation Using a Robust Edge-Stop Function with 2×2 Window Patch. In: 2020 IEEE 10th International Conference on System Engineering and Technology (ICSET), pp 224-227. IEEE. https://doi.org/10.1109/ICSET51301.2020.9265376
21. Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) CatBoost: Unbiased Boosting with Categorical Features. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp 6639-6649. Curran Associates Inc.
22. Rosa da Silva N, De Ridder M, Baetens JM, Van den Bulcke J, Rousseau M, Martinez Bruno O, De Baets B (2017) Automated classification of wood transverse cross-section micro-imagery from 77 commercial Central-African timber species. Annals of Forest Science, 74:1-14. https://doi.org/10.1007/s13595-017-0619-0
23. Ryszard SC (2007) Image feature extraction techniques and their applications for CBIR and biometrics systems. International Journal of Biology and Biomedical Engineering, 1(1):6-16.
24. Salau AO, Jain S (2019) Feature Extraction: A Survey of the Types, Techniques, Applications. In: 2019 International Conference on Signal Processing and Communication (ICSC), pp 158-164. IEEE. https://doi.org/10.1109/ICSC45622.2019.8938371
25. Silva JL, Bordalo R, Pissarra J, de Palacios P (2022) Computer vision-based wood identification: a review. Forests, 13(12):2041. https://doi.org/10.3390/f13122041
26. Sudhir R, Baboo LDSS (2011) An efficient CBIR technique with YUV color space and texture features. Computer Engineering and Intelligent Systems, 2(6):78-85.
27. Szeliski R (2022) Computer Vision: Algorithms and Applications. Springer Nature. https://doi.org/10.1007/978-3-030-34372-9
28. URL-1 http://www.kaggle.com/ Erişim tarihi: 21.07.2025.
29. Venugopal D, Sandhiya R, Swetha S, Sredhar R, Rohit N (2024) Machine Learning-Based Algorithm for Type of Wood Identification. In: 2024 International Conference on Emerging Research in Computational Science (ICERCS), pp 1-6. IEEE. https://doi.org/10.1109/ICERCS63125.2024.10895161
30. Yadav AR, Dewal ML, Anand RS, Gupta S (2013) Classification of Hardwood Species Using ANN Classifier. In: 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp 1-5. IEEE. https://doi.org/10.1109/NCVPRIPG.2013.6776231