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Leaf Disease Recognition Based on Channel Information Attention Network by Hongxia Deng; Dongsheng Luo; Zijing Zhou; Jinxiu Hou; Guanyu Qian; Haifang Li is a Computer Science article available to read on EtoBox.

What is Leaf Disease Recognition Based on Channel Information Attention Network about?

Aiming at the problem of the variety of plant leaf diseases and how to extract effective features, an attention network model fused with channel information is proposed to identify a variety of plant leaf diseases. Firstly, a residual structure based basic network is built for feature extraction, and in order to extract effective information, the feature is re-calibrated by integrating multiple channel information through the attention network. Then, the constraint information is added into the cross entropy function to accelerate the convergence of the model. Finally, the model is tested on the data sets of 16 diseases of four different plants. The results show that the recognition accuracy of the basic network model is 83.13%, while the accuracy increased by 4.64% after fusing the channel information network. Compared with other models, the fusion model improves the recognition accuracy by 9.72% and the model complexity is less than twice that of the optimal model in the comparison experiment.

Who reads Leaf Disease Recognition Based on Channel Information Attention Network?

Author

Hongxia Deng; Dongsheng Luo; Zijing Zhou; Jinxiu Hou; Guanyu Qian; Haifang Li

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)