Rice blast, rice sheath blight, and rice brown spot have become the most popular diseases in the cold areas of northern China. In order to further improve the accuracy and efficiency of rice disease diagnosis, a framework for automatic classification and recognition of rice diseases is proposed in this study. First, we constructed a training and testing data set including 1,500 images of rice blast, 1,500 images of rice sheath blight, and 1,500 images of rice brown spot, and 1,100 healthy images were collected from the rice experimental field. Second, the deep belief network (DBN) model is designed to include 15 hidden restricted Boltzmann machine layers and a support vector machine (SVM) optimized with switching particle swarm (SPSO). It is noted that the developed DBN and SPSO-SVM can simultaneously learn three proposed features including color, texture, and shape to recognize the disease type from the region of interest obtained by preprocessing the disease images. The proposed model leads to a hit rate of 91.37%, accuracy of 94.03%, and a false measurement rate of 8.63%, with the 10-fold cross-validation strategy. The value of the area under the receiver operating characteristic curve (AUC) is 0.97, whose accuracy is much higher than that of the conventional machine learning model. The simulation results show that the DBN and SPSO-SVM models can effectively extract the image features of rice diseases during recognition, and have good anti-interference and robustness.
An enhancement generator model with a progressive Wasserstein generative adversarial network and gradient penalized (PWGAN-GP) is proposed to solve the problem of low recognition accuracy caused by the lack of rice disease image samples in training CNNs. First, the generator model uses the progressive training method to improve the resolution of the generated samples step by step to reduce the difficulty of training. Second, to measure the similarity distance accurately between samples, a loss function is added to the discriminator that makes the generated samples more stable and realistic. Finally, the enhanced image datasets of three rice diseases are used for the training and testing of typical CNN models. The experimental results show that the proposed PWGAN-GP has the lowest FID score of 67.12 compared with WGAN, DCGAN, and WGAN-GP. In training VGG-16, GoogLeNet, and ResNet-50 with PWGAN-GP using generated samples, the accuracy increased by 10.44%, 12.38%, and 13.19%, respectively. PWGAN-GP increased by 4.29%, 4.61%, and 3.96%, respectively, for three CNN models over the traditional image data augmentation (TIDA) method. Through comparative analysis, the best model for identifying rice disease is ResNet-50 with PWGAN-GP in X2 enhancement intensity, and the average accuracy achieved was 98.14%. These results proved that the PWGAN-GP method could effectively improve the classification ability of CNNs.
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