Machine Learning Algorithms and Nondestructive Methods for Estimating Wood Density in Planted Forest Trees

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Abstract

Inferring forest properties is crucial for the timber industry, enabling efficient monitoring, predictive analysis, and optimized management. Nondestructive testing (NDT) methods have proven to be valuable tools for achieving these goals. Recent advancements in data analysis, driven by machine learning (ML) algorithms, have revolutionized this field. This study analyzed 492 eucalyptus trees, aged 3 to 7 years, planted in São Paulo, Brazil. Data from forest inventories were combined with results from ultrasound, drilling resistance, sclerometric impact, and penetration resistance tests. Seven machine learning algorithms were evaluated to compare their generalization capabilities with conventional statistical methods for predicting basic wood density. Among the models, Extreme Gradient Boosting (XGBoost) achieved the highest accuracy, with a coefficient of determination (R²) of 89% and a root mean square error (RMSE) of 10.6 kg·m⁻³. In contrast, the conventional statistical model, using the same parameters, yielded an R² of 33% and an RMSE of 26.4 kg·m⁻³. These findings highlight the superior performance of machine learning in nondestructive inference of wood properties, paving the way for its broader application in forest management and the timber industry.

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