Recognizing wheat seed varieties using hyperspectral imaging technology combined with multi-scale 3D convolution neural network

Abstract: A hyperspectral image classification model was proposed to detect wheat seeds using a Multi-Scale 3D Convolution Neural Network (MS-3DCNN) in this study, in order to identify wheat seed varieties quickly and accurately. A multi-scale 3D convolution module was used to learn the characteristics of wheat seed from hyperspectral images. A deep learning model was then established to predict wheat varieties. 3D Convolutional Neural Network (3DCNN) was utilized to simultaneously extract the spatial and spectral dimension features of hyperspectral images, compared with the traditional 2D Convolutional Neural Network (2DCNN). The kernel sizes of convolution were set as 5×5×5, 3×3×3, 5×5×3, and 3×3×5, respectively, considering that the characteristics of spectral dimension occupied a higher position in the application of hyperspectral image data. A Batch Normalization (BN) layer was added after each convolution layer to reduce the over-fitting of the model. The LeaKy_ReLU was adopted in the activation function to prevent neurons from being ineffective when the input was negative. A pooling layer and a fully connected layer were stacked on the last multi-scale convolution module. Finally, the Softmax activation function was used to predict the wheat varieties in the output layer. Dropout was introduced into the fully connected layer to reduce the risk of model overfitting. As such, a total of 6 000 samples were collected for 6 varieties of seeds (1 000 seeds per variety). Specifically, 700 seeds of each variety (4 200 seeds of the 6 varieties) were randomly selected as the training set, and the remaining 1 800 seeds were used as the test set during the specific training. 6 wheat varieties were also selected with certain connections in origin and genetic relationship to evaluate the influence of these factors on the classification model. Nevertheless, there was a relatively large amount of original hyperspectral image data, and a high data redundancy between adjacent hyperspectral bands. Successive Projections (SPA) were selected to combine with the average spectral characteristics of wheat seeds for the less data dimension. Subsequently, 22 optimal bands were selected from 300 bands, where the hyperspectral image data corresponding to the optimal bands was extracted to form a new hyperspectral image space. The reduced dimension data was input into the classification model of MS-3DCNN. The traditional hyperspectral classification model using Support Vector Machine (SVM), 2DCNN, 3DCNN, and Multi-Scale 2D Convolutional Neural Network (MS-2DCNN) were selected to compare the influence of 3D convolution and multi-scale convolution on model. The experimental results showed that the classification performed a higher classification accuracy using the MS-3DCNN model. SVM model using feature fusion, 2DCNN, 3DCNN, and MS-2DCNN models for the test sets achieved the accuracies of 88.33%, 94.17%, 95.17%, and 95.44%, respectively. Particularly, the MS-3DCNN model presented a relatively higher accuracy of 96.72%. Consequently, the improved model can be applied to identify and classify wheat seeds in modern intelligent agriculture.