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Tomato Leaf Disease Detection System Based on FC-SNDPN

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Abstract

In this study, taking the common diseases in tomato leaves, which are typical crops in southern China, as the research object, a FC-SNDPN (Fully Convolutional – Switchable Normalization Dual Path Networks) -based method for automatic identification and detection of crop leaf diseases is proposed to solve the problem that traditional image identification methods for crop diseases and insect pests heavily rely on artificial feature extraction and have a poor generalization ability for image recognition with a complex background. In order to reduce the influence of the complicated background on the recognition of crops diseases and insect pests image, A full convolutional network (FCN) algorithm based on VGG-16 model is used to segment the target crop image. Then an improved DPN (Dual-Path Networks) model is proposed to improve the ability of feature extraction. SNDPN combines the connection method between Desnet and Resnet layers, forms a neural network by using SN layer, and adaptively optimizes the parameters of the dual-path neural network by switching the normalized layer, which improves the versatility of the network for different types of diseases and insect pests and the training speed of the network. Finally, the identification accuracy of the proposed method of using FCN for foreground segmentation and SNDPN for identification is 97.59% on the augmentation data set, the result proves the effectiveness of our method.

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Acknowledgements

The author would like to thank China’s national 948 program for its funding. Meanwhile, the author would also like to thank Yang Xiaobo for his illustrations and experimental assistance, as well as Yi jizheng, a professor at Central South University of Forestry and Technology, for some helpful suggestions. The references are arranged according to the citations in the paper.

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Authors and Affiliations

  1. Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China

    Xibei Huang, Aibin Chen, Guoxiong Zhou, Xin Zhang, Ning Peng & Na Yan

  2. College of Information and Mechanical Engineering, Hunan International Economics University, Changsha, China

    Xibei Huang

  3. Hunan Provincial Key Laboratory of Urban Forest Ecology, Central South University of Forestry and Technology, Changsha, China

    Aibin Chen

  4. HuangFengQiao State-Owned Forest Farm, YouXian County, Hunan Province, China

    Jianwu Wang

  5. Beijing-DubIin International College at BJUT, Beijing, China

    Canhui Jiang

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  1. Xibei Huang
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  2. Aibin Chen
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Correspondence to Aibin Chen.

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Huang, X., Chen, A., Zhou, G. et al. Tomato Leaf Disease Detection System Based on FC-SNDPN. Multimed Tools Appl 82, 2121–2144 (2023). https://doi.org/10.1007/s11042-021-11790-3

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