Rice pest recognition based on multi-scale feature extraction depth residual network

ZHENG Xianrun; ZHENG Peng; WANG Wenxiu; CHENG Yahong; SU Yufeng

doi:10.7671/j.issn.1001-411X.202206037

Journal of South China Agricultural University > 2023 > 44(3): 438-446. > DOI: 10.7671/j.issn.1001-411X.202206037

ZHENG Xianrun, ZHENG Peng, WANG Wenxiu, et al. Rice pest recognition based on multi-scale feature extraction depth residual network[J]. Journal of South China Agricultural University, 2023, 44(3): 438-446. DOI: 10.7671/j.issn.1001-411X.202206037

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Rice pest recognition based on multi-scale feature extraction depth residual network

School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China

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Received Date: June 23, 2022
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
In the process of rice production, different control schemes need to be adopted for different pests. The accurate identification and classification of rice pests are the premise of formulating targeted control program.

Method
A deep residual network of multi-scale feature extraction was proposed based on the Res2Net structure, which could extract pest characteristics more accurately and realize rice pest identification in complex natural background. This network adopted an improved residual structure, replaced the original convolutional kernel with hierarchical class residual connections, increased the sensing field of each network layer, and could extract multi-scale features at a more fine-grained degree.

Result
The results showed that the model trained by this network could effectively identify rice pests in natural background. The average recognition accuracy of proposed model reached 92.023% on the self-built image dataset containing 22 kinds of the common rice pests, which was superior to the traditional ResNet, VGG and other networks.

Conclusion
This network can be applied to the automatic monitoring system of rice insect status, which provides a reference for the realization of machine vision monitoring of rice pests.
- Rice pest,
- Res2Net,
- Residual network,
- Deep learning,
- Image recognition,
- Image classification,
- Multi-scale feature

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References (21)

References

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Rice pest recognition based on multi-scale feature extraction depth residual network