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BACKGROUND As the primary food for nearly half of the world's population, rice is cultivated almost all over the world, especially in Asian countries. However, the farmers and planting experts have been facing many persistent agricultural challenges for centuries, such as different diseases of rice. The severe rice diseases may lead to no harvest of grains; therefore, a fast, automatic, less expensive and accurate method to detect rice diseases is highly desired in the field of agricultural information. RESULTS In this article, we study the deep learning approach for solving the task since it has shown outstanding performance in image processing and classification problem. Combining the advantages of both, the DenseNet pre‐trained on ImageNet and Inception module were selected to be used in the network, and this approach presents a superior performance with respect to other state‐of‐the‐art methods. It achieves an average predicting accuracy of no less than 94.07% in the public dataset. Even when multiple diseases were considered, the average accuracy reaches 98.63% for the class prediction of rice disease images. CONCLUSIONS The experimental results prove the validity of the proposed approach, and it is accomplished efficiently for rice disease detection. © 2020 Society of Chemical Industry

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Crop disease has a negative impact on food security. If diverse crop diseases are not identified in time, they can spread and influence the quality, quantity, and production of grain. Severe crop diseases can even result in complete failure of the harvest. Recent developments in deep learning, particularly convolutional neural networks (CNNs), have exhibited impressive performance in both image recognition and classification. In this study, we propose a novel network architecture, namely Mobile‐DANet, to identify maize crop diseases. Based on DenseNet, we retained the structure of the transition layers and used the depthwise separable convolution in dense blocks instead of the traditional convolution layers, and then embedded the attention module to learn the importance of interchannel relationship and spatial points for input features. In addition, transfer learning was used in model training. By this means, we improved the accuracy of the model while saving more computational power than deep CNNs. This model achieved an average accuracy of 98.50% on the open maize data set, and even with complicated backdrop conditions, Mobile‐DANet realized an average accuracy of 95.86% for identifying maize crop diseases on a local data set. The experimental findings show the effectiveness and feasibility of the Mobile‐DANet. Our data set is available at https://github.com/xtu502/maize-disease-identification.

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