DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification

Yang Chen, Xiaoyulong Chen, Jianwu Lin, Renyong Pan, Tengbao Cao, Jitong Cai, Dianzhi Yu, Tomislav Cernava and Xin Zhang ()
Additional contact information
Yang Chen: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Xiaoyulong Chen: College of Tobacco Science, Guizhou University, Guiyang 550025, China
Jianwu Lin: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Renyong Pan: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Tengbao Cao: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Jitong Cai: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Dianzhi Yu: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Tomislav Cernava: Institute of Environmental Biotechnology, Graz University of Technology, 8010 Graz, Austria
Xin Zhang: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China

Agriculture, 2022, vol. 12, issue 12, 1-22

Abstract: The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification.

Keywords: corn leaf disease; real scene; lightweight model; DFCANet (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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