Abstract
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.
Highlights
With the development of Internet of Things (IoT) technology, systems based on the IoT are often used in environmental monitoring because of its low cost and secure deployments, such as forest fire monitoring, crop growth monitoring, and marine climate monitoring [1]
The transfer learning based on the Visual Geometry Group (VGG) network model is proposed in Reference [11] to improve the image classification accuracy of crop diseases and ease the overfitting phenomenon, where the image features of tomato diseases are extracted through the VGG network model and classified through support vector machine (SVM) to detect tomato diseases, and excellent results are achieved
We propose a method of detecting crop diseases using a depthwise separable convolutional neural network (DSCNN), in which lightweight characteristics make it highly suitable for deployment in edge devices
Summary
With the development of Internet of Things (IoT) technology, systems based on the IoT are often used in environmental monitoring because of its low cost and secure deployments, such as forest fire monitoring, crop growth monitoring, and marine climate monitoring [1]. The transfer learning based on the Visual Geometry Group (VGG) network model is proposed in Reference [11] to improve the image classification accuracy of crop diseases and ease the overfitting phenomenon, where the image features of tomato diseases are extracted through the VGG network model and classified through support vector machine (SVM) to detect tomato diseases, and excellent results are achieved. We propose a method of detecting crop diseases using a depthwise separable convolutional neural network (DSCNN), in which lightweight characteristics make it highly suitable for deployment in edge devices. Aimed at the complexity of neural network model training, the training process is deployed on the cloud platform, and the obtained model and parameters will be sent back to the edge computing nodes for subsequent disease detection.
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