结合高光谱与CNN的小麦不完善粒识别方法

通过结合高光谱数据与卷积神经网络(CNN)实现小麦不完善粒(黑胚粒、虫蚀粒及破损粒)的快速准确鉴别。实验采集小麦正常粒(484粒)、黑胚粒(100粒)、虫蚀粒(100粒)及破损粒(100粒)在493~1 106 nm的116个波段的高光谱图像,每间隔5个波段抽取1个图像,分别建立24个波段的训练集,应用CNN建立不完善粒小麦的识别模型。实验结果显示,利用该识别模型,黑胚、虫蚀和破损粒的识别率分别保持在94%、95%和92%以上。在上述工作的基础上,进一步通过修改学习率和迭代次数改进CNN模型。优化后,黑胚、虫蚀及破损粒在各波段下的平均识别率分别提高了0.624%、0.47%和0.776%。将24个波段高光谱图像混合重新构建训练集,并重新训练CNN模型,黑胚、虫蚀及破损粒的总识别率则分别提高了0.31%、0.13%和0.46%。综上所述,基于高光谱数据和改进CNN模型可以有效提高小麦不完善粒的识别精度。

Identification method of unsound kernel wheat based on hyperspectral and convolution neural network

In this paper,a fast and accurate identification of unsound kernels of wheat (black embryo,wormhole and damaged) is introduced via the convolution neural network (CNN) model.The hyperspectral images of 116 bands in the range of 493 to 1 106 nm,which includes normal kernels (484 grains),black embryo kernels (100 grains),wormhole kernels (100 grains) and damaged kernels (100 grains),are collected.We take one sample out of every five bands to construct the training sets of the 24 bands respectively,and use the proposed model to establish the identification model of unsound kernels of wheat.Experimental results indicate that,by using the proposed model,the recognition rate of black embryo,wormhole and damaged grains is maintained at above 94％,95％ and 92％ respectively.We further improve the model by modifying the learning rate and the number of iterations,which end up improving the average recognition rate of black embryo,wormhole and damaged grains in each band by 0.624％、0.47％ and 0.776％.We combine the hyperspectral imagery of all 24 bands to reconstruct the training set and retrain the CNN model.The total recognition rate of black embryo,wormhole and damaged grains was increased by 0.31％,0.13％ and 0.46％,respectively.For our studies,we find that the accuracy of unsound kernels of wheat grain recognition,can be effectively improved using hyperspectral data and the proposed CNN model.