College of Information Science and Technology, Chengdu University, Chengdu 610106, China.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel). 2020 Jul 22;20(15):4091. doi: 10.3390/s20154091.
The original pattern recognition and classification of crop diseases needs to collect a large amount of data in the field and send them next to a computer server through the network for recognition and classification. This method usually takes a long time, is expensive, and is difficult to carry out for timely monitoring of crop diseases, causing delays to diagnosis and treatment. With the emergence of edge computing, one can attempt to deploy the pattern recognition algorithm to the farmland environment and monitor the growth of crops promptly. However, due to the limited resources of the edge device, the original deep recognition model is challenging to apply. Due to this, in this article, a recognition model based on a depthwise separable convolutional neural network (DSCNN) is proposed, which operation particularities include a significant reduction in the number of parameters and the amount of computation, making the proposed design well suited for the edge. To show its effectiveness, simulation results are compared with the main convolution neural network (CNN) models LeNet and Visual Geometry Group Network (VGGNet) and show that, based on high recognition accuracy, the recognition time of the proposed model is reduced by 80.9% and 94.4%, respectively. Given its fast recognition speed and high recognition accuracy, the model is suitable for the real-time monitoring and recognition of crop diseases by provisioning remote embedded equipment and deploying the proposed model using edge computing.
原始的作物病害模式识别和分类需要在田间收集大量数据，并通过网络将其发送到计算机服务器进行识别和分类。这种方法通常需要很长时间，成本很高，并且难以及时监测作物病害，导致诊断和治疗延误。随着边缘计算的出现，可以尝试将模式识别算法部署到农田环境中，并及时监测作物的生长情况。然而，由于边缘设备的资源有限，原始的深度识别模型难以应用。因此，在本文中，提出了一种基于深度可分离卷积神经网络（DSCNN）的识别模型，其操作特点包括参数数量和计算量的显著减少，使得所提出的设计非常适合边缘。为了展示其有效性，将仿真结果与主要卷积神经网络（CNN）模型 LeNet 和视觉几何组网络（VGGNet）进行了比较，结果表明，在保持高识别精度的前提下，所提出模型的识别时间分别减少了 80.9%和 94.4%。鉴于其快速的识别速度和高识别精度，该模型适用于通过提供远程嵌入式设备和使用边缘计算部署所提出的模型来实时监测和识别作物病害。
