Crop pest image classification based on improved densely connected convolutional network

Hongxing Peng; Huiming Xu; Zongmei Gao; Zhiyan Zhou; Xingguo Tian; Qianting Deng; Huijun He; Chunlong Xian

doi:10.3389/fpls.2023.1133060

Crop pest image classification based on improved densely connected convolutional network

Front Plant Sci. 2023 Apr 3:14:1133060. doi: 10.3389/fpls.2023.1133060. eCollection 2023.

Authors

Hongxing Peng^{1

2}, Huiming Xu¹, Zongmei Gao³, Zhiyan Zhou⁴, Xingguo Tian⁵, Qianting Deng⁶, Huijun He¹, Chunlong Xian⁷

Affiliations

¹ College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
² Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, Guangzhou, China.
³ Center for Precision and Automated Agricultural Systems, Department of Biological Systems Engineering, Washington State University, Prosser, WA, United States.
⁴ College of Engineering, South China Agricultural University, Guangzhou, China.
⁵ College of Food Science, South China Agricultural University, Guangzhou, China.
⁶ Department of Asset and Laboratory Management, South China Agricultural University, Guangzhou, China.
⁷ College of Economics and Management, South China Agricultural University, Guangzhou, China.

Abstract

Introduction: Crop pests have a great impact on the quality and yield of crops. The use of deep learning for the identification of crop pests is important for crop precise management.

Methods: To address the lack of data set and poor classification accuracy in current pest research, a large-scale pest data set named HQIP102 is built and the pest identification model named MADN is proposed. There are some problems with the IP102 large crop pest dataset, such as some pest categories are wrong and pest subjects are missing from the images. In this study, the IP102 data set was carefully filtered to obtain the HQIP102 data set, which contains 47,393 images of 102 pest classes on eight crops. The MADN model improves the representation capability of DenseNet in three aspects. Firstly, the Selective Kernel unit is introduced into the DenseNet model, which can adaptively adjust the size of the receptive field according to the input and capture target objects of different sizes more effectively. Secondly, in order to make the features obey a stable distribution, the Representative Batch Normalization module is used in the DenseNet model. In addition, adaptive selection of whether to activate neurons can improve the performance of the network, for which the ACON activation function is used in the DenseNet model. Finally, the MADN model is constituted by ensemble learning.

Results: Experimental results show that MADN achieved an accuracy and F1Score of 75.28% and 65.46% on the HQIP102 data set, an improvement of 5.17 percentage points and 5.20 percentage points compared to the pre-improvement DenseNet-121. Compared with ResNet-101, the accuracy and F1Score of MADN model improved by 10.48 percentage points and 10.56 percentage points, while the parameters size decreased by 35.37%. Deploying models to cloud servers with mobile application provides help in securing crop yield and quality.

Keywords: DenseNet-121; ensemble learning; pest image classification; representative batch normalization; selective kernel unit.

Grants and funding

This research was supported by Guangzhou Science and Technology Project (Grant No.202002020016,202102080337), Project of Rural Revitalization Strategy in Guangdong Province (Grant No. 2020KJ261), National Natural Science Foundation of China (Grant No. 61863011,32071912), Key Research and Development Program of Guangdong Province (Grant No.2019B0202170003), Natural Science Foundation of Guangdong Province (Grant No. 2018A030313330), Second Batch of Industry-Education Cooperation Collaborative Projects in 2019, Ministry of Education (Grant No. 201902062040), Guangzhou Key Laboratory of Intelligent Agriculture (201902010081), Applied Science and Technology Special Fund Project, Meizhou (Grant No. 2019B0201005) and Project of Ser-vice System of Agricultural Technology Extension and Policy advisory in Colleges and Universities, Agricultural and Rural Department of Guangdong Province (Grant No. F19406).