Tomato Leaf Disease Detection System Based on FC-SNDPN - Huang, Xibei; Chen, Aibin; Zhou, Guoxiong; Zhang, Xin; Wang, Jianwu; Peng, Ning; Yan, Na; Jiang, Canhui (Multimedia tools and applications, 2023)
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.