基于Inception与Residual组合网络的农作物病虫害识别

冯广; 孔立斌; 石鸣鸣; 贺敏慧; 何雅萱

doi:10.12052/gdutxb.190157

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基于Inception与Residual组合网络的农作物病虫害识别

更新时间：2026-05-27

- 基于Inception与Residual组合网络的农作物病虫害识别
- Crop Pest Recognition Based on Inception and Residual Combined Network
- 广东工业大学学报 2020年37卷第3期页码：17-22
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  冯广(1973–)，男，高级工程师，博士，主要研究方向为网络控制、机器学习、大数据
- 基金信息：
  
  国家自然科学基金资助项目(70971027)
- DOI：10.12052/gdutxb.190157
  中图分类号： TP391.41
- 网络首发：2020-05-12，
  
  纸质出版：2020
- 稿件说明：
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冯广, 孔立斌, 石鸣鸣, 贺敏慧, 何雅萱. 基于Inception与Residual组合网络的农作物病虫害识别[J]. 广东工业大学学报, 2020, 37(3): 17-22.

Feng Guang, Kong Li-bin, Shi Ming-ming, et al. Crop Pest Recognition Based on Inception and Residual Combined Network[J]. 2020, 37(3): 17-22.
冯广, 孔立斌, 石鸣鸣, 贺敏慧, 何雅萱. 基于Inception与Residual组合网络的农作物病虫害识别[J]. 广东工业大学学报, 2020, 37(3): 17-22. DOI： 10.12052/gdutxb.190157.

Feng Guang, Kong Li-bin, Shi Ming-ming, et al. Crop Pest Recognition Based on Inception and Residual Combined Network[J]. 2020, 37(3): 17-22. DOI： 10.12052/gdutxb.190157.

摘要

针对我国农作物病虫害识别方法中存在的速度慢、主观性强、所需专业知识要求高以及识别成本高等问题

提出一种基于Inception与Residual结构组合的Inception-resnet-v2网络模型的农作物病虫害识别方法

以实现精准高效的农作物病虫害识别。网络使用residual结构

采用跨层连接方式将低层特征与高层特征进行组合学习以增加网络深度。同时加入了Inception结构

既能保持网络结构的稀疏性

又能利用密集矩阵的高计算性能

加快了训练速度。最后通过Softmax分类器进行多分类预测。与传统方法相比

本文方法收敛速度更快

不仅准确率达到96.67%、精确度达到90.77%、召回率达到89.72%

还使病虫害识别的不同类别更加均衡

改善了传统方法中对特定类别识别效果差的问题。

Abstract

Aiming at the problems of slow speed

subjectivity

high requirement of professional knowledge and high recognition cost in the identification methods of crop pests and diseases in China

a crop pest and disease identification method is proposed based on Inception-resnet-v2 network model combining Inception and Residual structure. To achieve accurate and efficient crop pest identification

the network uses a residual structure to combine low-level features with high-level features to increase network depth. At the same time

the Inception structure is added

which not only maintains the sparsity of the network structure

but also utilizes the high computational performance of the dense matrix to speed up the training. Finally

the multi-class prediction is performed by the Softmax classifier. Compared with the traditional method

the proposed method converges faster

not only with an accuracy rate of 96.67%

but also with an accuracy of 90.77% and a recall rate of 89.72%. It also makes the different categories of pest and disease identification more balanced

improving the specific methods in the traditional methods

by which the recognition effect is poor.

关键词

Keywords

references

赵国华, 王婷. 基于ARM的作物病虫害自动识别系统设计[J]. 南方农机, 2019, 50(5): 62.

CHAI S, LI Z J. Detection of tomato pests and diseases based on transfer learning [J]. Computer Engineering and Design, 2019, 40(6): 1701-1705.

SUN J. Study on early warning of insect pests in tea yard based on image target detection [J]. Tea in Fujian, 2018, 40(12): 11.

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Image net classification with deep convolutional neural networks[C]//NIPS 2012: Proceedings of the 25th International Conference on Neural Information Processing Systems. North Miami Beach, FL: Curran Associates Inc, 2012: 1097-1105.

SZEGEDY C, TOSHEV A, ERHAN D. Deep neural networks for object detection[C]// Proceedings of the 2013 International Conference on Neural Information Processing Systems. Cambridge, MA: MIT Press, 2013: 2553-2561.

ERHAN D, SZEGEDY C, TOSHEV A, et al. Scalable object detection using deep neural networks[C]//CVPR 2014: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2014: 2155-2162.

HE K M, ZHANG X Y, REN S Q, et al . Spatial pyramid pooling in deep convolutional networks for visual recognition [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.

ROSS G. Fast R-CNN[C]// The IEEE International Conference on Computer Vision (ICCV). Washington, DC: IEEE Computer Society, 2015: 1440-1448.

LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//ECCV 2016: Proceedings of the 2016 European Conference on Computer Vision, LNCS 9905. Cham: Springer, 2016: 21-37.

WANG J J, ZHANG W, LIU L Z, et al . Summary of crop diseases and pests image recognition technology [J ] . Computer Engineering Science, 2014, 36(7): 1363-1370.

SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//The IEEE Conference on Computer Vision and Pattern Recognition. Washington (CVPR), DC: IEEE Computer Society, 2015: 1-9.

HE K, ZHANG X, REN S, et al. Deep Residual Learning for Image Recognition[C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Washington, DC: IEEE Computer Society, 2016: 770-778.

CAI J X, WANG R H, HUANG Y Y. Application of brightness normalization in image processing [J]. Journal of Guangdong University of Technology, 2008, 25(4): 65-68.

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