TY - JOUR
T1 - DFCANet
T2 - A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification
AU - Chen, Yang
AU - Chen, Xiaoyulong
AU - Lin, Jianwu
AU - Pan, Renyong
AU - Cao, Tengbao
AU - Cai, Jitong
AU - Yu, Dianzhi
AU - Cernava, Tomislav
AU - Zhang, Xin
N1 - Funding Information:
This research was funded by National Key Research and Development Plan Key Special Projects, grant number 2021YFE0107700, National Nature Science Foundation of China, grant numbers 61865002 and 31960555, Guizhou Science and Technology Program, grant number 2019-1410, and Outstanding Young Scientist Program of Guizhou Province, grant number KY2021-026. In addition, the study received support by the Program for Introducing Talents to Chinese Universities, 111Program, grant number D20023.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - corn leaf disease
KW - DFCANet
KW - lightweight model
KW - real scene
UR - http://www.scopus.com/inward/record.url?scp=85144710442&partnerID=8YFLogxK
U2 - 10.3390/agriculture12122047
DO - 10.3390/agriculture12122047
M3 - Article
AN - SCOPUS:85144710442
SN - 2077-0472
VL - 12
JO - Agriculture (Switzerland)
JF - Agriculture (Switzerland)
IS - 12
M1 - 2047
ER -