Abstract
The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases' spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model's ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases.
Recommended Citation
T. Liao et al., "MDAM-DRNet: Dual Channel Residual Network with Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection," Frontiers in Plant Science, vol. 13, article no. 869524, Frontiers Media, Jul 2022.
The definitive version is available at https://doi.org/10.3389/fpls.2022.869524
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Color Feature Path; Detection of Strawberry Leaf Diseases; ELU; Multidirectional Attention Mechanism; Multidirectional Attention Mechanism Dual Channel Residual Network; Texture Feature Path
International Standard Serial Number (ISSN)
1664-462X
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2022 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
08 Jul 2022
Comments
This work was supported by Changsha Municipal Natural Science Foundation (Grant No. kq2014160); in part by the Natural Science Foundation of Hunan Province (Grant No. 2021JJ41087); in part by Hunan Key Laboratory of Intelligent Logistics Technology (Grant No. 2019TP1015).