Title: Research on crop diseases classification model based on MobileNet

Authors: Zejun Wang; Fangfang Zhang; Fengying Ma; Peng Ji; Lei Kou; Michaël Antonie Van Wyk; Maoyong Cao

Addresses: School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China ' School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China ' School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China ' School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, Shandong, China ' Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China ' School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China

Abstract: To address the problems of large size and low-recognition accuracy of the Convolutional Neural Networks (CNNs) for crop disease recognition, this paper proposes an improved model of MobileNet called MobileNet-LR-SE. The model firstly uses the LR structure to reverse the feature information and get the new feature information. These together with the original feature information form the input to the feature fusion layer. The LR structure introduces residual connection and uses the Leaky ReLU activation function. Secondly, it embeds the SE module to complete the final image classification. The LR structure solves the problem of ignoring negative feature information in the training process of ordinary neural networks. The SE module improves the attention of the network model to the useful channels. Experiments show that the MobileNet-LR-SE model has a high-accuracy rate when evaluated on the two crop disease data sets, with the number of network parameters of only 2.01M.

Keywords: deep learning; convolutional neural network; lightweight neural network; crop diseases; image classification.

DOI: 10.1504/IJWMC.2023.130406

International Journal of Wireless and Mobile Computing, 2023 Vol.24 No.2, pp.169 - 180

Received: 06 Jan 2022
Received in revised form: 14 Apr 2022
Accepted: 07 Sep 2022
Published online: 19 Apr 2023 *

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