The combination of computer technology and non-destructive testing technology can facilitate the development of forestry in a more intelligent direction. In this paper, a Shapley additive explanations (SHAP)-based method is used to analyse the importance of band features in the near-infrared spectrum of black walnut wood, which ranges from 900 to 1650 nm. The spectral data from the SHAP analysis are fed into an integrated framework of machine learning algorithms based on four different theories. In the comparison tests, three different pre-processed NIR spectral data are entered into the integrated framework. The result of the SHAP analysis shows that the wavelengths that are positively correlated with the air-dry density of black walnut are 1354.59, 1400.23, 1341.51, 1426.26, 1413.25 nm. The model predictions show that the SHAP-treated spectral data outperformed the other two treatments for each model. For the SHAP-treated spectral data, the KNN model gives the best results with an R 2 of 0.947 and an MSE of 0.0010.

中文翻译：

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基于近红外分析预测黑胡桃木的气干密度

计算机技术与无损检测技术的结合，可以促进林业向更加智能化的方向发展。本文采用基于沙普利加性解释（SHAP）的方法来分析黑胡桃木近红外光谱（900 至 1650 nm）波段特征的重要性。来自 SHAP 分析的光谱数据被输入到基于四种不同理论的机器学习算法的集成框架中。在对比测试中，将三种不同的预处理近红外光谱数据输入到集成框架中。SHAP分析结果表明，与黑胡桃木气干密度正相关的波长为1354.59、1400.23、1341.51、1426.26、1413.25 nm。模型预测表明，对于每个模型，SHAP 处理的光谱数据优于其他两种处理。对于 SHAP 处理的光谱数据，KNN 模型给出了最佳结果右 2为 0.947，MSE 为 0.0010。