Wood density is a pivotal indicator for evaluating wood quality. Therefore, through the accurate prediction of Pinus sylvestris wood density, the utilization efficiency of wood can be effectively enhanced, bearing significant implications for the rational utilization of wood resources and the cultivation of forest trees.

This study focuses on Pinus sylvestris wood and utilizes NIR non-destructive testing technology. It employs feature extraction algorithms including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), as well as grid search algorithm-support vector machine regression (Grid-SVM), genetic algorithm-support vector machine regression (GA-SVM), and particle swarm optimization-support vector machine regression (PSO-SVM). These algorithms are sequentially applied for feature band extraction and SVM modeling. Consequently, an ideal near-infrared prediction model for Pinus sylvestris wood density is developed.

Ninety-seven samples of Pinus sylvestris wood were divided into training and testing sets in a 1∶3 ratio. Grid-SVM, GA-SVM, and PSO-SVM models were established using the full spectrum of near-infrared data. To simplify the near-infrared models, three feature extraction algorithms-uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA) were sequentially applied to select spectral bands. Subsequently, Grid-SVM, GA-SVM, and PSO-SVM algorithms were employed to model the selected wavelengths. Ultimately, it was found that the GA-SVM model, utilizing feature bands extracted by UVE, exhibited the highest accuracy and best performance for predicting Pinus sylvestris wood density, with an R² value of 0.910 8 and RMSEP of 0.005 9 for the prediction set.

Optimized the SVM model for Pinus sylvestris wood density using near-infrared spectroscopy feature extraction algorithms, thereby achieving rapid and precise prediction of wood density. This approach not only minimizes wastage of wood resources but also maximizes their utility and value. Moreover, it provides a theoretical foundation and technical guidance for enhancing and optimizing the cultivation of superior tree species and forest management practices. These advancements propel the wood industry towards greater efficiency and sustainability.