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
corresponding author:
ZHANG Zhenghua, E-mail:zhangzh@yzu.edu.cn
About author:
PAN Chenlu, E-mail:2300935324@qq.com
Supported by:
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: https://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 |
1 | 徐春春, 纪龙, 陈中督, 等. 2022年我国水稻产业发展分析及2023年展望[J]. 中国稻米, 2023, 29(2): 1-4. |
XU C C, JI L, CHEN Z D, et al. Analysis of China's rice industry in 2022 and the outlook for 2023[J]. China rice, 2023, 29(2): 1-4. | |
2 | 刘震, 纪明妹, 郭志顶, 等. 图像识别技术在病虫害防治方面的应用与展望[J]. 沧州师范学院学报, 2022, 38(1): 119-123. |
LIU Z, JI M M, GUO Z D, et al. Application and prospect of image recognition technology in pest control[J]. Journal of Cangzhou normal university, 2022, 38(1): 119-123. | |
3 | 朱成宇. 基于轻量级卷积神经网络的水稻病害识别[D]. 南宁: 广西大学, 2022. |
ZHU C Y. Rice desease recognition based on lightweight convolutional neural network[D]. Nanning: Guangxi University, 2022. | |
4 | BRAHIMI M, BOUKHALFA K, MOUSSAOUI A. Deep learning for tomato diseases: Classification and symptoms visualization[J]. Applied artificial intelligence, 2017, 31(4): 299-315. |
5 | RAHMAN C R, ARKO P S, ALI M E, et al. Identification and recognition of rice diseases and pests using convolutional neural networks[J]. Biosystems engineering, 2020, 194: 112-120. |
6 | 李淼, 王敬贤, 李华龙, 等. 基于CNN和迁移学习的农作物病害识别方法研究[J]. 智慧农业, 2019, 1(3): 46-55. |
LI M, WANG J X, LI H L, et al. Method for identifying crop disease based on CNN and transfer learning[J]. Smart agriculture, 2019, 1(3): 46-55. | |
7 | SHARMA V, TRIPATHI A K, MITTAL H. DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection[J]. Ecological informatics, 2023, 75: ID 102025. |
8 | MONU B, DILIP K, SUNIL K. Bell pepper leaf disease classification with LBP and VGG-16 based fused features and RF classifier[J]. International journal of information technology, 2022, 15(1): 465-475. |
9 | 王东方, 汪军. 基于迁移学习和残差网络的农作物病害分类[J]. 农业工程学报, 2021, 37(4): 199-207. |
WANG D F, WANG J. Crop disease classification with transfer learning and residual networks[J]. Transactions of the Chinese society of agricultural engineering, 2021, 37(4): 199-207. | |
10 | 高友文, 周本君, 胡晓飞. 基于数据增强的卷积神经网络图像识别研究[J]. 计算机技术与发展, 2018, 28(8): 62-65. |
GAO Y W, ZHOU B J, HU X F. Research on image recognition of convolution neural network based on data enhancement[J]. Computer technology and development, 2018, 28(8): 62-65. | |
11 | HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2017: 2261-2269. |
12 | HAN K, WANG Y H, TIAN Q, et al. GhostNet: more features from cheap operations[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2020: 1577-1586. |
13 | WANG Q L, WU B G, ZHU P F, et al. ECA-net: Efficient channel attention for deep convolutional neural networks[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2020: 11531-11539. |
14 | 张善文, 张晴晴, 李萍. 基于改进深度卷积神经网络的苹果病害识别[J]. 林业工程学报, 2019, 4(4): 107-112. |
ZHANG S W, ZHANG Q Q, LI P. Apple disease identification based on improved deep convolutional neural network[J]. Journal of forestry engineering, 2019, 4(4): 107-112. | |
15 | 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251. |
ZHOU F Y, JIN L P, DONG J. Review of convolutional neural network[J]. Chinese journal of computers, 2017, 40(6): 1229-1251. | |
16 | 罗大为, 方建军, 刘艳霞. 基于通道域注意力机制的特征融合方式[J]. 东北师大学报(自然科学版), 2021, 53(3): 44-48. |
LUO D W, FANG J J, LIU Y X. Feature fusion methods based on channel domain attention mechanism[J]. Journal of northeast normal university (natural science edition), 2021, 53(3): 44-48. | |
17 | 杨莲, 石宝峰. 基于Focal Loss修正交叉熵损失函数的信用风险评价模型及实证[J]. 中国管理科学, 2022, 30(5): 65-75. |
YANG L, SHI B F. Credit risk evaluation model and empirical research based on focal loss modified cross-entropy loss function[J]. Chinese journal of management science, 2022, 30(5): 65-75. | |
18 | LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]// 2017 IEEE International Conference on Computer Vision (ICCV). Piscataway, NJ, USA: IEEE, 2017: 2999-3007. |
19 | 卫雅娜, 王志彬, 乔晓军, 等. 基于注意力机制与EfficientNet的轻量化水稻病害识别方法[J]. 中国农机化学报, 2022, 43(11): 172-181. |
WEI Y N, WANG Z B, QIAO X J, et al. Lightweight rice disease identification method based on attention mechanism and EfficientNet[J]. Journal of Chinese agricultural mechanization, 2022, 43(11): 172-181. | |
20 | SENGUPTA A, YE Y T, WANG R, et al. Going deeper in spiking neural networks: VGG and residual architectures[EB/OL]. arXiv: , 2018. |
21 | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2016: 770-778. |
22 | ZOPH B, VASUDEVAN V, SHLENS J, et al. Learning transferable architectures for scalable image recognition[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2018: 8697-8710. |
23 | 黎英, 宋佩华. 迁移学习在医学图像分类中的研究进展[J]. 中国图象图形学报, 2022, 27(3): 672-686. |
LI Y, SONG P H. Review of transfer learning in medical image classification[J]. Journal of image and graphics, 2022, 27(3): 672-686. | |
24 | 黄浩, 徐海华, 王羡慧, 等. 自动发音错误检测中基于最大化F1值准则的区分性特征补偿训练算法[J]. 电子学报, 2015, 43(7): 1294-1299. |
HUANG H, XU H H, WANG X H, et al. Maximum F1 score criterion based discriminative feature compensation training algorithm for automatic mispronunciation detection[J]. Acta electronica sinica, 2015, 43(7): 1294-1299. |
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