基于老化不同时间的稻种的生理学和物理学特性，提出一种基于多尺度小波变换和灰色神经网络的稻种发芽率红外热预测模型，实现稻种发芽率的快速、无损检测，解决传统发芽实验法实验周期长、操作复杂等问题。从不同发芽率稻种的胚芽部位提取144组数据，通过多尺度小波变换，分析逼近信号和细节信号，得出第3层细节信号(d3) 贡献最大。以第3层细节信号作为模型的输入，随机分为校正集和预测集，校正集96组，预测集48组。分析和比较老化不同时间的稻种的红外热差异，通过偏最小二乘算法(PLS)、BP神经网络、径向基神经网络(RBFNN)和灰色神经网络(GNN)，建立稻种发芽率红外热预测模型。结果表明，GNN建立的稻种发芽率模型预测效果最优，其中校正集相关系数(RC)和标准偏差(SEC)分别为0.9619、2.5013，预测集相关系数(RP)和标准偏差(SEP)分别为0.9554、2.4172，相关性达到较高水平且误差较小。研究表明采用小波分解和灰色神经网络建立稻种发芽率红外热预测模型的方法是可行的。

On the basis of physiological and physical properties of rice seeds with different aging time, an infrared thermal model for testing rice seed germination rate by multi-scale wavelet transform and grey neural network is proposed to realize fast and non-destructive detection of rice seed germination rate, and to solve the problems of long experimental period, complex operation resulted from traditional germination rate test methods. 144 samples are extracted from germ section of different rice seeds. Detail signal of the third layer wavelet decomposition (d3) is the greatest contribution by analyzing approximation signal and detail signal through a multi-scale wavelet transform. So the detail signal of the third layer wavelet decomposition is used as the model input, and the samples are randomly divided into a calibration set (96 samples) and a prediction set (48 samples). The infrared thermal difference of rice seeds with different aging time is analyzed and compared through partial least squares (PLS), back propagation (BP) neural network, radial basis function neural network (RBFNN) and grey neural network (GNN) to establish infrared thermal prediction models of rice seed germination rate. The results show that the optimal model of germination rate is constructed by GNN artificial neural network, by which the correlation coefficient (RC) and standard deviation (SEC) of the calibration set are 0.9619 and 2.5013 respectively, and the correlation coefficient (RP) and standard deviation (SEP) of the prediction set are 0.9554 and 2.4172 respectively, the relevance reaches a higher level and the error is small. The experimental results show that adopting wavelet decomposition and GNN to establish the infrared thermal prediction model of rice seed germination rate is feasible.