改进注意力机制嵌入PR-Net模型的水稻病害识别仿真

doi:10.16182/j.issn1004731x.joss.23-0322

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (6): 1322-1333.doi: 10.16182/j.issn1004731x.joss.23-0322

改进注意力机制嵌入PR-Net模型的水稻病害识别仿真

路阳¹(), 刘鹏飞¹, 许思源¹, 刘启旺¹, 顾福谦¹, 王鹏²^,³^,⁴

^1.黑龙江八一农垦大学信息与电气工程学院, 黑龙江大庆 163319
^2.东北石油大学黑龙江省网络化与智能控制重点实验室, 黑龙江大庆 163318
^3.东北石油大学人工智能能源研究院, 黑龙江大庆 163318
^4.东北石油大学三亚海洋油气研究院, 海南三亚 572024

收稿日期:2023-03-21 修回日期:2023-05-29 出版日期:2024-06-28 发布日期:2024-06-19
第一作者简介:路阳(1976-)，男，教授，博导，博士，研究方向为复杂系统智能故障诊断及模式识别。E-mail：luyanga@sina.com
基金资助:
国家自然科学基金(U21A2019);黑龙江省自然科学基金联合引导项目(LH2020F042);黑龙江省博士后科研启动基金(LBH-Q17134);海南省科技专项(ZDYF2022SHFZ105)

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

Lu Yang¹(), Liu Pengfei¹, Xu Siyuan¹, Liu Qiwang¹, Gu Fuqian¹, Wang Peng²^,³^,⁴

^1.College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
^2.Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China
^3.Artificial Intelligence Energy Institute, Northeast Petroleum University, Daqing 163318, China
^4.Sanya Research Institute of Offshore Oil and Gas, Northeast Petroleum University, Sanya 572024, China

Received:2023-03-21 Revised:2023-05-29 Online:2024-06-28 Published:2024-06-19

摘要/Abstract

摘要：

针对现有的CNN模型在水稻叶部病害的识别中准确率较低的问题，提出了一种结合并行结构和残差结构的混合卷积神经网络模型PRC-Net(parallel residual with coordinate attention network)。引入并行结构，提高卷积的感受野；结合残差结构，使特征信息完整的连续传递；在骨干模型PR-Net中嵌入改进的空间注意力机制，增强对不同尺度病斑特征信息的凝聚程度；为进一步提升病害识别的准确率，并减少模型的训练时间和推理时间，通过改变加权方式对模型结构进行优化。仿真结果表明：与InceptionResNetV2等分类模型相比，PRC-Net具有更少的训练参数、更短的训练时间和更高的识别精度，性能优于其他作物病害识别模型。

关键词: 水稻叶部病害, PRC-Net(parallel residual with coordinate attention network), 卷积神经网络, 注意力机制, 图像识别

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.

Key words: rice leaf disease, PRC-Net, convolution neural network, attention mechanism, image recognition

中图分类号:

TP391

路阳,刘鹏飞,许思源等 . 改进注意力机制嵌入PR-Net模型的水稻病害识别仿真[J]. 系统仿真学报, 2024, 36(6): 1322-1333.

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.

图/表 17

图1

图2

表1

PR-Net结构相关参数

层名	内核	步长	输出形状
Input			(None, 256, 256, 3)
Conv2D_1	$7 × 7$	2	(None, 128, 128, 128)
MaxPooling2D_1	$3 × 3$	2	(None, 64, 64, 128)
Conv2D_2	$3 × 3$	1	(None, 64, 64, 64)
MaxPooling2D_2	$3 × 3$	2	(None, 32, 32, 64)
PR-Block A			(None, 32, 32, 192)
PR-Block B			(None, 32, 32, 384)
PR-Block C			(None, 32, 32, 1280)
GlobalAveragePooling2D			(None, 1280)
Softmax			(None, 6)

表1

图3

图4

图5

表2

图6

表3

表4

表5

图7

表6

图8

图9

表7

表8

参考文献 23

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类别	训练集数量	测试集数量
合计	10 973	2 863
稻瘟病	1 689	422
白叶枯病	1 439	480
胡麻斑病	1 478	369
纹枯病	1 415	354
东格鲁病	1 329	332
健康叶片	3 623	906

模型	训练准确率/%	训练损失率	测试准确率/%	测试损失率
VGG16	99.61	0.007 5	98.48	0.028 3
ResNet50	99.82	0.054 0	98.27	0.041 0
InceptionV3	99.24	0.009 6	98.13	0.022 2
InceptionResNetV2	99.87	0.002 4	98.51	0.020 1
PR-Net	99.55	0.006 0	99.10	0.013 8
CA + PR-Net	99.62	0.004 9	97.03	0.035 2
Improved CA + PR-Net	99.56	0.004 7	99.27	0.007 6
PRC-Net	99.56	0.005 4	99.65	0.007 3
CA+MobileNetV2	99.36	0.009 7	91.39	0.080 5

模型	指标	0	1	2	3	4	5
VGG16	精确率	0.97	0.97	0.97	0.99	0.99	0.99
VGG16	召回率	0.98	0.98	0.96	0.99	0.99	0.99
ResNet50	精确率	0.98	0.98	0.99	0.97	0.99	0.97
ResNet50	召回率	0.97	0.97	0.96	0.99	0.99	1.00
InceptionV3	精确率	0.97	0.95	1.00	1.00	0.99	1.00
InceptionV3	召回率	0.98	0.98	0.90	0.99	1.00	1.00
Inception-ResNetV2	精确率	0.98	1.00	0.95	1.00	1.00	1.00
Inception-ResNetV2	召回率	0.99	0.97	1.00	0.98	0.99	1.00
PRC-Net	精确率	1.00	0.99	1.00	1.00	1.00	1.00
PRC-Net	召回率	0.99	1.00	1.00	1.00	1.00	1.00

核尺寸	训练准确率/%	训练损失率	测试准确率/%	测试损失率
1	99.56	0.004 7	99.27	0.007 6
2	99.47	0.005 1	98.67	0.011 2
3	99.51	0.005 6	98.53	0.012 6
4	98.95	0.009 3	98.44	0.011 2
5	98.93	0.008 5	98.42	0.012 9

模型	模型尺寸/MB	平均迭代时间/min	总训练时间/min	推理时间/s
VGG16	172	1.42	42.72	0.012
ResNet50	285	1.36	40.72	0.013
InceptionV3	259	1.34	40.13	0.011
Inception-ResNetV2	630	2.93	87.90	0.024
PR-Net	43.8	1.02	30.72	0.008
CA + PR-Net	44.5	1.59	47.25	0.013
Improved CA+PR-Net	86.3	1.64	49.22	0.014
PRC-Net	86.3	1.29	38.72	0.010

改进注意力机制嵌入PR-Net模型的水稻病害识别仿真

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

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参考文献 23

相关文章 15

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Metrics

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模型	番茄	玉米	土豆
VGG16	94.24	94.10	96.76
ResNet50	92.56	94.62	95.37
InceptionV3	94.54	94.62	97.69
Inception-ResNetV2	96.16	95.90	96.06
PRC-Net	99.12	95.77	99.07

模型	数据集	准确率/%
Optimized DenseNet-121	2(14/38)	98.70
VGG-ICNN	2	99.16
PRC-Net	2	99.07

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