Wood defect detection is a critical aspect of quality control in the woodworking industry. This work introduces Deep Wood Inspect, a pioneering system that leverages the capabilities of deep learning for the precise identification and classification of defects in wooden materials. The proposed methodology utilizes densely connected Convolutional Neural Networks (CNNs), specifically DenseNet, to analyze high-resolution images of wood surfaces, providing an automated and efficient solution for defect detection. By integrating advanced image processing techniques with machine learning algorithms, Deep Wood Inspect not only enhances the accuracy of defect identification but also accelerates the inspection process, reducing manual labor and minimizing human error. The system's adaptability to various types of wood and defect categories further contributes to its robustness, making it a valuable tool for both large- scale manufacturers and smaller woodworking enterprises seeking to uphold high standards of quality control.