基于CNN神经网络的小麦不完善粒高光谱检测

利用高光谱成像技术对小麦不完善粒进行无损检测。以932个小麦为样本,其中正常粒样本486个、破损粒样本170个、虫蚀粒样本149个及黑胚粒样本127个为研究对象,通过高光谱图像采集系统采集样本的光谱信息,然后从每个样本的116个波段中选取30个波段,建立基于深度学习的卷积神经网络(convolutional neural networks,CNN)模型。实验中的CNN采用2个卷积层,第1层采用大小为3×3的32个卷积核,第2层采用大小为5×5的64个卷积核,池化层采用最大池,激活函数采用修正线性单元,为避免过拟合,在全连接层后面接入dropout层,参数设置为0.5,其他卷积参数均为默认值,得到校正集总识别率为100.00%,测试集总识别率为99.98%。最后,以支持向量机(support vector machine,SVM)为基线模型进行对比,从116个波段中选取90个波段进行建模,测试集总识别率为94.73%。通过实验对比可以看出,CNN模型比SVM模型识别率高。研究表明CNN模型能够实现对小麦不完善粒的准确、快速、无损检测。

Hyperspectral Detection of Unsound Kernels of Wheat Based on Convolutional Neural Network

A nondestructive examination was conducted to identify unsound kernels by using hyperspectral imaging technology. Based on 932 wheat samples including 486 normal samples, 170 damaged samples, 149 worm-eaten samples and 127 black germ kernel samples, a hyperspectral image acquisition system was used for collecting hyperspectral information, and then a convolutional neural network (CNN) was established based on 30 wavebands selected from 116 wavebands for each sample. The CNN model comprised two convolution layers. The first layer consisted of 32 convolution kernels (3 × 3) and the second layer consisted of 64 convolution kernels (5 × 5). The pooling layer was developed with the maximum pool. The activation function was developed with rectified linear units (ReLu). To avoid overfitting, a dropout layer was linked to the fully connected layer, and the parameter was set as 0.5. When other parameters remained default, recognition rates for the calibration and test sets were 100.00% and 99.98% respectively. Finally, a support vector machine (SVM) model was built and compared with the CNN model. The SVM model developed with 90 wavebands selected from 116 wavebands showed a recognition rate of 94.73% for the test set. The recognition rate of the CNN model was better than that of the SVM model. Thus, this research showed that the CNN model allowed for accurate, rapid and nondestructive detection of unsound wheat kernels.