DenseNet Framework for Wood Quality Assessment: Predicting Defects in Wood Images

Wood is an essential natural resource that is widely used in many different industries, but it is especially important in the building and furniture industries. On the other hand, flaws in wood like rot, cracks, and knots can seriously affect the material's overall quality and structural integrity. Conventional techniques for examining wood for flaws are frequently laborious and arbitrary. Modern technology, particularly deep learning, has been used more frequently in recent years to automate the process of diagnosing wood flaws. This research introduces a novel approach to wood quality assessment using the Dense Net deep learning architecture. The goal is to use image analysis to determine whether or not wood samples contain flaws. A carefully selected collection of photos of wood samples with and without defects is created, and preprocessing methods including scaling, normalization, and augmentation are used to it. Next, the DenseNet architecture is put into practice, improving information flow throughout the network by utilizing its distinct dense connectivity pattern. To guarantee strong performance on untested data, the model is assessed on a different test set after being trained on the dataset.