Innovative computational techniques for accurate internal defect detection in trees: A stress wave tomography approach enhanced by machine learning

dc.contributor.advisor Tunga, Burcu
dc.contributor.author YIldızcan, Ecem Nur
dc.contributor.authorID 509211206
dc.contributor.department Mathematics Engineering
dc.date.accessioned 2025-01-30T11:52:16Z
dc.date.available 2025-01-30T11:52:16Z
dc.date.issued 2024-06-10
dc.description Thesis (M.Sc.) -- İstanbul Technical University, Graduate School, 2024
dc.description.abstract The detection of internal defects in trees holds critical importance given the health of forest ecosystems and the industrial significance of wood products. The identification of these internal defects without damaging the wood is a significant factor in the forestry industry and in the production of wood products. While traditional methods often require cutting or processing the wood, non-invasive techniques such as stress wave tomography offer the possibility of identifying internal defects without disrupting the wood's structure. This contributes both to the sustainable management of forest resources and to the improvement of wood product quality. A branch of artificial intelligence, machine learning algorithms allow computer systems to analyze data, recognize patterns, make decisions, and solve problems. These algorithms are critical tools in analyzing large datasets obtained from non-invasive techniques like stress wave tomography, and in accurately detecting and classifying internal defects. In this thesis, an algorithm design capable of generating stress wave tomography based on ray segmentation and machine learning has been developed for the purpose of detecting internal defects in trees. A two-stage algorithm has been proposed based on data obtained from stress waves produced by sensors mounted on trees and on the segmented propagation rays generated from these data. In the first step, a ray segmentation method maps the velocity of stress waves to create segmented sensors. In the second step, data obtained from these segmented rays are processed using K-Nearest Neighbors (KNN) and Gaussian Process Classifier (GPC) algorithms to create a tomographic image of defects within the tree. The algorithm carries the potential to detect internal defects in wood without causing damage and provides more precise results compared to traditional methods. Implemented using the Python programming language, the algorithm equips researchers with the ability to understand and analyze the internal structure of trees. This method stands out as a practical tool for contributing to forest health assessment and conservation through stress wave tomography. During experiments, data from four real trees were collected via sensors, and an algorithm was developed to generate four sets of synthetic defective tree data in the sensor's data format. Real tree data was provided by Istanbul University Cerrahpaşa Faculty of Forestry. All tree data were individually used to feed the proposed defect detection algorithm, and the outputs were transformed into tomographic images. Success rates above 90% were achieved for all evaluation metrics. Compared to related studies, the results showed improvements ranging from 7% to 22% relative to the literature. This thesis aims to contribute to the development of the sustainable wood industry by offering a new approach to detecting internal tree defects. Although the results obtained are quite good compared to the results in the scientific literature, it is thought that even better results will be obtained by optimizing the parameters of the algorithm or by differentiating the machine learning algorithms integrated into the method.
dc.description.degree M.Sc.
dc.identifier.uri http://hdl.handle.net/11527/26306
dc.language.iso en_US
dc.publisher Graduate School
dc.sdg.type Goal 2: Zero Hunger
dc.sdg.type Goal 3: Good Health and Well-being
dc.sdg.type Goal 8: Decent Work and Economic Growth
dc.sdg.type Goal 12: Responsible Consumption and Production
dc.subject tree
dc.subject ağaç
dc.subject ecosystem
dc.subject ekosistem
dc.subject machine learning
dc.subject makine öğrenimi
dc.title Innovative computational techniques for accurate internal defect detection in trees: A stress wave tomography approach enhanced by machine learning
dc.title.alternative Ağaçlarda iç kusurların doğru tespiti için yenilikçi hesaplamalı teknikler: Makine öğrenimi ile geliştirilmiş bir stres dalgası tomografi yaklaşımı
dc.type Master Thesis
Dosyalar
Orijinal seri
Şimdi gösteriliyor 1 - 1 / 1
thumbnail.default.alt
Ad:
509211206.pdf
Boyut:
14.86 MB
Format:
Adobe Portable Document Format
Açıklama
Lisanslı seri
Şimdi gösteriliyor 1 - 1 / 1
thumbnail.default.placeholder
Ad:
license.txt
Boyut:
1.58 KB
Format:
Item-specific license agreed upon to submission
Açıklama