The application aids the lumber industry in identifying timber types, grading quality, identifying flaws, and optimizing resources for sustainable and efficient timber processing.

Abstract

The timber sector is essential to the economy; however, it continues to depend on manual and often inaccurate techniques for identifying defects, grading, and cutting. As demand increases and sustainability issues become more pressing, there is an urgent requirement for smart, automated solutions. This project aims to tackle these inefficiencies by utilizing artificial intelligence to improve precision and decision-making in the inspection and processing of timber. To tackle this issue, TimberTrack was created a web-based application that leverages deep learning and image processing techniques for the detection of timber defects, grading, and optimization of cutting processes. The system utilizes YOLOv5 for real-time identification of defects across five categories (Crack, Hole, Knot, Combined, Good), relying on annotated datasets. A convolutional neural network (CNN) classifier assesses timber quality, while a tailored rule-based logic suggests the most efficient cutting paths to reduce waste. The backend is developed using Python, and the frontend is constructed with ReactJS, enabling users to upload images, view outcomes, and produce reports. The performance of the model was assessed through metrics such as Precision, Recall, F1-Score, and mean Average Precision (mAP). The detection model attained a mAP of 91.3% at an Intersection over Union (IoU) of 0.5, while the classifier achieved an accuracy of 94.7%. TimberTrack exhibits significant promise as a dependable, AI-powered solution for improving quality control within the timber sector. Future enhancements may include expanding the dataset, incorporating real-time video capabilities, and integrating explainable AI features.