Integrating image analysis and ensemble learning for predictive modeling of wood density and MC using line scanner and camera systems

• Novel method predicts wood density and moisture content through 2D image analysis. • Examining wood color and physical attributes profoundly boosts prediction accuracy. • Ensemble Learning integrates multiple models to effectively forecast wood density. • Precise moisture content prediction is vital for optimal wood processing outcomes. • By fusing density and moisture content predictions improves overall accuracy. Conventional wood quality assessments often involve invasive methods that increase costs and weaken material integrity. Recent studies using machine learning, particularly convolutional neural networks (CNNs) and ensemble learning, have shown high accuracy in predicting wood density and mechanical properties from images. However, real-time evaluation of moisture content (MC) across different species remains underexplored. This research introduces a non-destructive approach that integrates 2D image analysis with an advanced ensemble learning model to predict both wood density and MC, using high-resolution images from Line Scanner and Camera Systems to extract key color features and wood attributes. A novel real-time MC determination method employing a gravimetric formula based on actual sample dimensions and weights further enhances immediate wood evaluation. The model achieves reliable predictions with a Coefficient of Determination (R 2 ) of 0.94 for oven-dry density and 0.85 for MC, and Root Mean Square Errors (RMSE) of 0.01 g/cm 3 and 8.33 %, respectively, offering a precise, viable tool for real-time wood quality and moisture assessment, and advancing sustainable industry practices.