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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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Abstract-A smartphone has more advanced computing ability and connectivity than basic featured phones. Presently, we are moving from the Internet society to a mobile society where more and more access to the information is required. This has resulted in a mobile security which is no longer immanent, but imperative. Smartphone authentication has received substantial attention of the research community for the past several years because there have been modern developments beyond the classical PINs and passwords making user authentication more challenging. In this paper, we critically analyze the attacks and the vulnerabilities in smartphones' authentication mechanisms. A comparative analysis of different authentication techniques along with the usage of the different authentication methods is discussed which lead the end-user towards choosing the most suitable and customizable authentication technique.

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