Objective The micro-drilling resistance method is one of the principal techniques for non-destructive evaluation of wood density in standing trees. However, existing regression-based prediction models built upon this technique are susceptible to multi-source disturbances arising from instrumentation, operators, and environmental conditions, leading to poor stability and limited generalizability. These shortcomings constrain its application in engineering practice (e.g., wood grading and rapid field testing) and impede reliable analysis in scientific research. To enhance the robustness and practical applicability of the micro-drilling resistance method for wood density prediction, this study aims to explore a modeling approach that accommodates measurement uncertainty.

Method By transforming the modeling concept, the continuous density prediction problem was reconstructed as a discrete interval classification task. Taking Schima superba as an example, based on 481 sets of micro-drilling resistance–density paired data retained after data preprocessing and outlier removal, a predictive model was developed using an interval classification approach based on machine learning (LightGBM), and systematically compared with several conventional regression models. By outputting density intervals, this method accommodates measurement fluctuations, thereby enhancing the model’s adaptability.

Result The developed interval classification model achieved an overall accuracy of 0.876 and a macro-average F₁ score of 0.878, significantly outperforming all conventional regression models used for comparison in this study (the best-performing regression model attained an R² of 0.771). The model performed consistently across all density intervals, with precision ranging from 0.769 to 1.000. For the high- and low-density end intervals, which frequently require unambiguous discrimination in practical wood grading, the classification accuracy reached or exceeded 84%, demonstrating strong discriminative capability. These results indicate that the interval classification strategy yields more robust grading outcomes than conventional point estimation.

Conclusion The proposed and validated “interval classification” modeling strategy, by reformulating continuous density prediction as an interval classification task, effectively enhances the practical applicability of the micro-drilling resistance method for predicting wood density. The density grades output by this strategy are intuitively suited to rapid engineering judgment, while its classification probability outputs provide structured data for scientific research. This study offers a new machine learning modeling perspective focused on robustness and usability, for addressing the common problem of model instability in the application of the micro-drilling resistance method.