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Insect pest control is considered as a significant factor in the yield of commercial crops. Thus, to avoid economic losses, we need a valid method for insect pest recognition. In this paper, we proposed a feature fusion residual block to perform the insect pest recognition task. Based on the original residual block, we fused the feature from a previous layer between two 1 ×1 convolution layers in a residual signal branch to improve the capacity of the block. Furthermore, we explored the contribution of each residual group to the model performance. We found that adding the residual blocks of earlier residual groups promotes the model performance significantly, which improves the capacity of generalization of the model. By stacking the feature fusion residual block, we constructed the Deep Feature Fusion Residual Network (DFF-ResNet). To prove the validity and adaptivity of our approach, we constructed it with two common residual networks (Pre-ResNet and Wide Residual Network (WRN)) and validated these models on the Canadian Institute For Advanced Research (CIFAR) and Street View House Number (SVHN) benchmark datasets. The experimental results indicate that our models have a lower test error than those of baseline models. Then, we applied our models to recognize insect pests and obtained validity on the IP102 benchmark dataset. The experimental results show that our models outperform the original ResNet and other state-of-the-art methods.


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DFF-ResNet: An Insect Pest Recognition Model Based on Residual Networks

Show Author's information Wenjie LiuGuoqing Wu( )Fuji Ren( )Xin Kang
School of Information Science and Technology and School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China, and also with the Faculty of Engineering, Tokushima University, Tokushima 770-8506, Japan
School of Information Science and Technology, Nantong University, Nantong 226019, China
Faculty of Engineering, Tokushima University, Tokushima 770-8506, Japan

Abstract

Insect pest control is considered as a significant factor in the yield of commercial crops. Thus, to avoid economic losses, we need a valid method for insect pest recognition. In this paper, we proposed a feature fusion residual block to perform the insect pest recognition task. Based on the original residual block, we fused the feature from a previous layer between two 1 ×1 convolution layers in a residual signal branch to improve the capacity of the block. Furthermore, we explored the contribution of each residual group to the model performance. We found that adding the residual blocks of earlier residual groups promotes the model performance significantly, which improves the capacity of generalization of the model. By stacking the feature fusion residual block, we constructed the Deep Feature Fusion Residual Network (DFF-ResNet). To prove the validity and adaptivity of our approach, we constructed it with two common residual networks (Pre-ResNet and Wide Residual Network (WRN)) and validated these models on the Canadian Institute For Advanced Research (CIFAR) and Street View House Number (SVHN) benchmark datasets. The experimental results indicate that our models have a lower test error than those of baseline models. Then, we applied our models to recognize insect pests and obtained validity on the IP102 benchmark dataset. The experimental results show that our models outperform the original ResNet and other state-of-the-art methods.

Keywords:

insect pest recognition, deep feature fusion, residual network, image classification
Received: 20 July 2020 Accepted: 22 September 2020 Published: 16 November 2020 Issue date: December 2020
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Publication history

Received: 20 July 2020
Accepted: 22 September 2020
Published: 16 November 2020
Issue date: December 2020

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© The authors 2020

Acknowledgements

This work was partially supported by the Research Clusters Program of Tokushima University and JSPS KAKENHI (No. 19K20345).

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