针对稻谷谷壳率和整精米率的预测问题，以46个品种的稻谷样本为研究对象，采集近红外漫反射光谱信息，使用2阶小波消噪和Z-score归一化对光谱数据进行预处理，利用Kennard-Stone法划分样本集。通过竞争自适应重加权采样法筛选出与谷壳率和整精米率相关的特征波长，并根据多元线性回归理论建立了稻谷谷壳率和整精米率的预测模型，结果表明，稻谷谷壳率的近红外特征波长为21个，整精米率的特征波长为28个；两模型的决定系数分别为0.998 3和0.998 7，定标标准差分别为0.112 9和0.982 1，相对偏差分别为0.51%和2.34%。

In order to predict husk content and rice production yield, 46 species of paddy were studied. After the near infrared diffuse reflectance spectroscopy, samples were preprocessed by wavelet denoising and Z-score normalizing. Kennard-Stone method was used to select the sample set. The competitive adaptive reweighted sampling was applied to screening the key wavelengths associated with the husk content and rice production yield from paddy. Finally, the prediction models of the husk content and rice yield were established with the multiple linear regression. The results indicated that 21 near infrared key wavelengths were associated with the husk content, and 28 key wavelengths were associated with rice yield. The R2 of two prediction models were 0.998 3 and 0.998 7. The root mean square error of calibrations were 0.112 9 and 0.982 1, and the relative deviation were 0.51% and 2.34%, respectively.