Simulation of Rice Disease Recognition Based on Improved Attention Mechanism Embedded in PR-Net Model

Authors

Yang Lu, College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaFollow
Pengfei Liu, College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Siyuan Xu, College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Qiwang Liu, College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Fuqian Gu, College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Peng Wang, Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China; Artificial Intelligence Energy Institute, Northeast Petroleum University, Daqing 163318, China; Sanya Research Institute of Offshore Oil and Gas, Northeast Petroleum University, Sanya 572024, China

Abstract

Abstract: Aiming at the low accuracy of existing CNN models in identifying rice leaf diseases, a hybrid convolutional neural network model PRC-Net (parallel residual with coordinate attention network) combining parallel structure and residual structure is proposed. A parallel structure is introduced to improve the receptive field of convolution, and the residual structure is combined to achieve the complete and continuous transmission of feature information. An improved spatial attention mechanism is embedded into the backbone model PR-Net to enhance the degree of aggregation of lesion feature information at different scales. In order to further improve the accuracy of disease identification and reduce the training and reasoning time of the model, the model structure is optimized by changing the weighting method. Simulation results show that, compared to the classification models such as InceptionResNetV2, PRC-Net has fewer training parameters, shorter training time, and higher recognition accuracy, which is superior to the other crop disease identification models.

Recommended Citation

Lu, Yang; Liu, Pengfei; Xu, Siyuan; Liu, Qiwang; Gu, Fuqian; and Wang, Peng (2024) "Simulation of Rice Disease Recognition Based on Improved Attention Mechanism Embedded in PR-Net Model," Journal of System Simulation: Vol. 36: Iss. 6, Article 6.
DOI: 10.16182/j.issn1004731x.joss.23-0322
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss6/6

First Page

1322

Last Page

1333

CLC

TP391

Recommended Citation

Lu Yang, Liu Pengfei, Xu Siyuan, et al. Simulation of Rice Disease Recognition Based on Improved Attention Mechanism Embedded in PR-Net Model[J]. Journal of System Simulation, 2024, 36(6): 1322-1333.

DOI

10.16182/j.issn1004731x.joss.23-0322