Open Access

ARTICLE

Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease Recognition

by Chang Zhang¹, Ruiwen Ni¹, Ye Mu^1,2,3,4, Yu Sun^1,2,3,4,*, Thobela Louis Tyasi⁵

1 College of Information Technology, Jilin Agricultural University, Changchun, 130118, China
2 Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center, Changchun, 130118, China
3 Jilin Province Intelligent Environmental Engineering Research Center, Changchun, 130118, China
4 Jilin Province Information Technology and Intelligent Agriculture Engineering Research Center, Changchun, 130118, China
5 Department of Agricultural Economics and Animal Production, University of Limpopo, Private Bag X 1106, Sovenga, 0727, Polokwane, South Africa

* Corresponding Author: Yu Sun. Email:

Computers, Materials & Continua 2023, 74(1), 983-994. https://doi.org/10.32604/cmc.2023.027269

Received 13 January 2022; Accepted 23 March 2022; Issue published 22 September 2022

Abstract

In the field of agricultural information, the identification and prediction of rice leaf disease have always been the focus of research, and deep learning (DL) technology is currently a hot research topic in the field of pattern recognition. The research and development of high-efficiency, high-quality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective. This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network (CNN) model and proposes a recognition model that combines a multi-scale convolution module with a neural network model based on Visual Geometry Group (VGG). The accuracy and loss of the training set and the test set are used to evaluate the performance of the model. The test accuracy of this model is 97.1% that has increased 5.87% over VGG. Furthermore, the memory requirement is 26.1 M, only 1.6% of the VGG. Experiment results show that this model performs better in terms of accuracy, recognition speed and memory size.

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Keywords

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