This study presents an offline mobile diagnosis system for citrus pests and diseases by compression convolutional neural network. Recently, with the growth of labelled data, the deep neural network incites the revolutionary change with a quantum leap in various fields. Benefiting from the backpropagation method, the proper network structure can automatically extract high-level representations and find corresponding labels. The authors made use of the advantages of the deep neural network to design an android application, which can be installed in any stand-alone devices to instantaneously identify the citrus pests and diseases. The proposed diagnosis system has three characteristics: low cost, low latency and high accuracy. These characteristics contribute to make the professional offline prediction for avoiding further economic loss caused by disease spreading. In order to validate the proposed system, the authors conducted thorough evaluations on two data sets, ‘citrus pests and diseases’, CIFAR, which show the superiority of the proposed approach in terms of the accuracy and the number of model parameters.

中文翻译：

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深度压缩神经网络的柑橘病虫害离线移动诊断系统

本研究提出了一种利用压缩卷积神经网络的柑橘病虫害离线诊断系统。近年来，随着标记数据的增长，深度神经网络在各个领域掀起了革命性的飞跃。得益于反向传播方法，适当的网络结构可以自动提取高级表示并找到相应的标签。作者利用深度神经网络的优势设计了一个android应用程序，该应用程序可以安装在任何独立的设备中，以即时识别柑橘类病虫害。所提出的诊断系统具有三个特征：低成本，低等待时间和高精度。这些特征有助于做出专业的离线预测，从而避免了疾病传播所造成的进一步经济损失。为了验证所提出的系统，作者对两个数据集“柑橘病虫害” CIFAR进行了全面评估，这些数据集显示了所提出方法在准确性和模型参数数量方面的优越性。