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

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.