The accurate identification of wood species plays a vital role in key industrial processes, such as timber processing operations, quality assurance inspections, and inventory tracking. In industrial production settings, wood specimen images are frequently acquired using cost-constrained mobile imaging devices, inevitably compromising image quality through motion-induced blurring, sensor noise artifacts, and suboptimal resolution characteristics. These technical limitations severely degrade the operational effectiveness of traditional recognition systems, creating cascading inefficiencies across downstream production workflows. To overcome these limitations, we have developed an industrial-grade deep learning framework specifically engineered to deliver reliable wood species identification under challenging real-world imaging conditions. The proposed framework incorporates two core technical components to address image quality degradation in industrial settings: (1) an enhanced Very Deep Super-Resolution (VDSR) network for detail reconstruction, and (2) a comprehensive multi-scale augmentation pipeline for robust feature learning. In this study, evaluation experiments were conducted utilizing wood image datasets of three distinct species-Pterocarpus santalinus, Pterocarpus tinctorius, and Gluta sp.-which were captured by three different imaging devices corresponding to high, medium, and low-quality acquisition conditions. The experimental validation demonstrates substantial performance gains, with our enhanced framework achieving a 25% absolute accuracy improvement over the baseline ResNet-50 model when processing low-quality input images. This research establishes a cost-effective and scalable technical foundation for quality recognition systems in industrial imaging applications, with direct integration potential into intelligent quality control systems for manufacturing operations.