Rice pest identification based on multi-scale double-branch GAN-ResNet - Hu, Kui; Liu, YongMin; Nie, Jiawei; Zheng, Xinying; Zhang, Wei; Liu, Yuan; Xie, TianQiang (Frontiers in Plant Science, 2023)
Rice production is crucial to the food security of all human beings, and how rice pests and diseases can be effectively prevented in and timely detected is a hotspot issue in the field of smart agriculture. Deep learning has become the preferred method for rice pest identification due to its excellent performance, especially in the aspect of autonomous learning of image features. However, in the natural environment, the dataset is too small and vulnerable to the complex background, which easily leads to problems such as overfitting, and too difficult to extract the fine features during the process of training. To solve the above problems, a Multi-Scale (Multi-Scale) Dual-branch (Dual-branch) structural rice pest identification model (GAN-MSDB-ResNet) based on a generative adversarial network and improved ResNet was proposed. Based on the ResNet model, the ConvNeXt residual block was introduced to optimize the calculation ratio of the residual blocks, and the double-branch structure was constructed to extract disease features of different sizes in the input disease images, which it adjusts the size of the convolution kernel of each branch. In the complex natural environment, data pre-processing methods such as random brightness and motion blur, and data enhancement methods such as mirroring, cropping, and scaling were used to allow the dataset of 5,932 rice disease images captured from the natural environment to be expanded to 20,000 by the dataset in this paper. A new GAN-MSDB-ResNet model was trained on the new dataset to identify four common rice diseases. The experimental results showed that the recognition accuracy of the new GAN-MSDB-ResNet rice pest recognition model, which was proposed for the first time, improved by 2.66% compared with the original ResNet model. Under the same experimental conditions, the new model had the best performance when compared with classical networks such as AlexNet, VGG, DenseNet, ResNet, and Transformer, and its recognition accuracy could be as high as 99.34%. The model use enhances the flexibility of the sensor in crop disease image acquisition without the need to consider the effects of factors such as light and strong wind on disease images.