Spatial-spectral attention-enhanced Res-3D-OctConv for corn and weed identification utilizing hyperspectral imaging and deep learning

•A method for identifying corn seedlings and weeds based on hyperspectral images was proposed.•A spatial-spectral attention-enhanced Res-3D-OctConv model was proposed.•The proposed method has higher precision than traditional methods and deep learning methods. Corn production is an important basis to ensure the world food security, and weeds in the field will cause corn production decline. Therefore, in order to quickly recognize corn and weed in the field, a model was proposed by combining hyperspectral image with deep learning method. However, there are some problems in hyperspectral image, such as high redundancy of adjacent spectra and insufficient feature information extraction. In order to solve the above problems, the four principal components based on principal component analysis (PCA) were firstly extracted in this paper, so as to decrease the information redundancy between adjacent spectra. Secondly, the residual three-dimensional octave convolution (Res-3D-OctConv) was used to excavate the spatial information from the frequency components, while taking into account the spectral information. Finally, spatial and spectral attention models were introduced to highlight important spatial information and spectral information. At the same time, the spatial information and spectral information was integrated by cross fusion. Experimental results show that the recognition accuracy of the proposed model is 98.56 %, which is 8.65 % and 10.20 % higher than that of k-nearest neighbor (KNN) and support vector machine (SVM) respectively. The recognition result of the proposed model is further compared with that of 3D residual network (3D-ResNet) and 3D convolutional neural network (3D-CNN), and the recognition precision of the proposed model in this paper is increased by 1.40 % and 1.02 % compared with 3D-CNN and 3D-ResNet, respectively. The results show that the proposed model can better recognize the hyperspectral images of corn and weed.