Smart Agriculture ›› 2023, Vol. 5 ›› Issue (2): 45-55.doi: 10.12133/j.smartag.SA202305002
• Topic--Machine Vision and Agricultural Intelligent Perception • Previous Articles Next Articles
PAN Chenlu(), ZHANG Zhenghua(), GUI Wenhao, MA Jiajun, YAN Chenxi, ZHANG Xiaomin
Received:
2023-05-07
Online:
2023-06-30
CLC Number:
PAN Chenlu, ZHANG Zhenghua, GUI Wenhao, MA Jiajun, YAN Chenxi, ZHANG Xiaomin. Rice Disease and Pest Recognition Method Integrating ECA Mechanism and DenseNet201[J]. Smart Agriculture, 2023, 5(2): 45-55.
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URL: http://www.smartag.net.cn/EN/10.12133/j.smartag.SA202305002
Table 4
Prediction accuracy comparison of different models for each classification in rice pest identification experiments
Model | BrownSpot Acc | Hispa Acc | LeafBlast Acc | Health Acc | Acc |
---|---|---|---|---|---|
VGG-16 | 0.8438 | 0.6875 | 0.7812 | 0.8063 | 0.7869 |
ResNet50 | 0.6875 | 0.5625 | 0.8125 | 0.9313 | 0.7983 |
NasNet(4@1056) | 0.7500 | 0.5156 | 0.7969 | 0.8688 | 0.7699 |
DenseNet201 | 0.7500 | 0.5156 | 0.8438 | 0.9437 | 0.8125 |
GE-DenseNet | 0.7500 | 0.7188 | 0.7969 | 0.9313 | 0.8352 |
Table 5
Performance comparison of different models for rice pest identification experiments
Model | Acc | Macro Avg P | Macro Avg R | Macro Avg F1 | Size/MB | Calculation Volume/GFLOPs |
---|---|---|---|---|---|---|
VGG-16 | 0.7869 | 0.7761 | 0.7797 | 0.7747 | 154.17 | 30.76 |
ResNet50 | 0.7983 | 0.8163 | 0.7484 | 0.7725 | 482.02 | 7.93 |
NasNet(4@1056) | 0.7699 | 0.7647 | 0.7328 | 0.7429 | 218.45 | 1.24 |
DenseNet201 | 0.8125 | 0.8354 | 0.7633 | 0.7850 | 437.43 | 8.77 |
GE-DenseNet | 0.8352 | 0.8451 | 0.7992 | 0.8169 | 437.68 | 8.78 |
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