Maize is a globally important crop, and reliable identification of seed varieties is vital for breeding and quality assurance. To overcome the limitations of conventional methods, this study developed a non-destructive approach integrating hyperspectral imaging (HSI) and deep learning. A Residual Mamba One-Dimensional Convolutional Neural Network (RM1DNet) is proposed, which integrates residual and Mamba modules to enhance feature learning. RM1DNet achieved 94.85% accuracy in classifying 20 maize seed varieties, outperforming traditional classifiers and baseline deep models. These results demonstrate the robustness and efficiency of RM1DNet, highlighting its potential to advance intelligent seed variety identification using hyperspectral data.

As a pillar grain crop in China’s agriculture, the yield and quality of corn are directly related to food security and the stable development of the agricultural economy. Corn varieties from different regions have significant differences inblade, staminate and root cap characteristics, and these differences provide a basis for variety classification. However, variety characteristics may be mixed in actual cultivation, which increases the difficulty of identification. Deep learning classification research based on corn nodulation features can help improve classification accuracy, optimize planting management, enhance production efficiency, and promote the development of breeding and production technologies. In this study, we established a dataset of maize plants at the elongation stage containing 31,000 images of 40 different types, including corn leaves, staminates, and root caps, and proposed a DenXt framework model. Representative Batch Normalization (RBN) is introduced into the DenseNet-121 model to improve the generalization ability of the model, and the SE module and deep separable convolution are integrated to enhance the feature representation and reduce the computational complexity, and the Dropout regularization is introduced to further improve the generalization ability of the model and reduce the overfitting. The proposed network model achieves a classification accuracy of 97.79%, which outperforms VGG16, Mobilenet V3, ResNet50 and ConvNeXt image classification models in terms of performance. Compared with the original DenseNet 121 network model, the DenXt model improved the classification accuracy by 3.23% and reduced the parameter count by 32.65%. In summary, the new approach addresses the challenges of convolutional neural networks and provides easy-to-deploy lightweight networks to support corn variety recognition applications.