Wheat-Seed Variety Recognition Based on the GC_DRNet Model

Wheat is a significant cereal for humans, with diverse varieties. The growth of the wheat industry and the protection of breeding rights can be promoted through the accurate identification of wheat varieties. To recognize wheat seeds quickly and accurately, this paper proposes a convolutional neural network-based image-recognition method for wheat seeds, namely GC_DRNet. The model is based on the ResNet18 network and incorporates the dense network idea by changing its residual module to a dense residual module and introducing a global contextual module, reducing the network model’s parameters and improving the network’s recognition accuracy. Experiments were conducted on the self-constructed wheat-seed dataset and the publicly available dataset CIFAR-100 by combining GC_DRNet with network models such as ResNet18, ResNet34, ResNet50, and DenseNet121. The GC_DRNet model achieved a recognition accuracy of 96.98% on the wheat-seed dataset, which was improved by 2.34%, 1.43%, 2.05%, and 1.77% compared to ResNet18, ResNet34, ResNet50, and DenseNet121, respectively. On the CIFAR-100 dataset, the recognition accuracy of the GC_DRNet model was 80.77%, which improved the accuracy of ResNet18, ResNet34, ResNet50, and DenseNet121 by 8.19%, 1.6%, 9.59%, and 16.29%, respectively. Analyzing the confusion-matrix results of the wheat-seed dataset, the average recognition precision of the test set was 97.02%, the recall rate was 96.99%, and the F1 value was 96.98%. The parameter size of the GC_DRNet model was smaller than that of the other three models, only 11.65MB. The experimental results show that the GC_DRNet has a high level of recognition accuracy and detection capability for images of wheat seeds and provides technical support for wheat-seed identification.