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Identification of tomato leaf diseases using convolutional neural network with multi-scale and feature reuse

文献类型： 外文期刊

作者： Li, Peng 1 ; Zhong, Nan 1 ; Dong, Wei 2 ; Zhang, Meng 1 ; Yang, Dantong 1 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China

2.Anhui Acad Agr Sci, Agr Econ & Informat Res Inst, Hefei 230031, Anhui, Peoples R China

关键词： tomato diseases; convolutional neural network; confusion matrix; multi-scale; feature reuse

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.4； 五年影响因子：2.8 ）

ISSN： 1934-6344

年卷期： 2023 年 16 卷 6 期

页码：

收录情况： SCI

摘要： Various diseases seriously affect the quality and yield of tomatoes. Fast and accurate identification of disease types is of great significance for the development of smart agriculture. Many Convolution Neural Network (CNN) models have been applied to the identification of tomato leaf diseases and achieved good results. However, some of these are executed at the cost of large calculation time and huge storage space. This study proposed a lightweight CNN model named MFRCNN, which is established by the multi-scale and feature reuse structure rather than simply stacking convolution layer by layer. To examine the model performances, two types of tomato leaf disease datasets were collected. One is the laboratory-based dataset, including one healthy and nine diseases, and the other is the field-based dataset, including five kinds of diseases. Afterward, the proposed MFRCNN and some popular CNN models (AlexNet, SqueezeNet, VGG16, ResNet18, and GoogLeNet) were tested on the two datasets. The results showed that compared to traditional models, the MFRCNN achieved the optimal performance, with an accuracy of 99.01% and 98.75% in laboratory and field datasets, respectively. The MFRCNN not only had the highest accuracy but also had relatively less computing time and few training parameters. Especially in terms of storage space, the MFRCNN model only needs 2.7 MB of space. Therefore, this work provides a novel solution for plant disease diagnosis, which is of great importance for the development of plant disease diagnosis systems on low-performance terminals.

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