Rice is a staple food for almost half of the world’s population, and the stability and sustainability of rice production plays a decisive role in food security. Diseases are a major cause of loss in rice crops. The timely discovery and control of diseases are important in reducing the use of pesticides, protecting the agricultural eco-environment, and improving the yield and quality of rice crops. Deep convolutional neural networks (DCNNs) have achieved great success in disease image classification. However, most models have complex network structures that frequently cause problems, such as redundant network parameters, low training efficiency, and high computational costs. To address this issue and improve the accuracy of rice disease classification, a lightweight deep convolutional neural network (DCNN) ensemble method for rice disease classification is proposed. First, a new lightweight DCNN model (called CG-EfficientNet), which is based on an attention mechanism and EfficientNet, was designed as the base learner. Second, CG-EfficientNet models with different optimization algorithms and network parameters were trained on rice disease datasets to generate seven different CG-EfficientNets, and a resampling strategy was used to enhance the diversity of the individual models. Then, the sequential least squares programming algorithm was used to calculate the weight of each base model. Finally, logistic regression was used as the meta-classifier for stacking. To verify the effectiveness, classification experiments were performed on five classes of rice tissue images: rice bacterial blight, rice kernel smut, rice false smut, rice brown spot, and healthy leaves. The accuracy of the proposed method was 96.10%, which is higher than the results of the classic CNN models VGG16, InceptionV3, ResNet101, and DenseNet201 and four integration methods. The experimental results show that the proposed method is not only capable of accurately identifying rice diseases but is also computationally efficient.

To achieve fast and accurate identification of rice diseases in the field, we propose an automatic rice disease classifier, in which the process of characterizing rice diseases is visualized and analyzed by a deconvolutional neural network. An AlexNet model, pretrained by ImageNet, is constructed and trained on rice disease images to classify them. After the training is completed, the signal is repositioned to the corresponding position of the input image by a deconvolutional neural network corresponding to the AlexNet structure. The set of pixels that contribute most to the prediction of the convolutional neural network is identified from the deconvolution visualization map. The experimental results demonstrated the effectiveness of the proposed method. The classifier achieved an accuracy of 90.03% for the rice disease dataset, which was 8.39% and 16.78% higher than the accuracies achieved by the LeNet and BP neural networks, respectively. The features of the middle layer of the convolutional neural network perform a hierarchical transformation from low-level information, such as color, to high-level information, such as contours and edges of disease spots. This transformation process matches the criteria for the actual identification of rice diseases. The proposed method lays the foundation for the accurate identification of crop diseases and the design and adjustment of deep convolutional neural network structures.