Citation:
Li, Z.; Jiang, X.; Jia, X.;
Duan, X.; Wang, Y.; Mu, J.
Classification Method of Significant
Rice Pests Based on Deep Learning.
Agronomy 2022, 12, 2096. https://
doi.org/10.3390/agronomy12092096
Academic Editors: Saeid Homayouni,
Yacine Bouroubi and Karem
Chokmani
Received: 10 August 2022
Accepted: 30 August 2022
Published: 1 September 2022
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agronomy
Article
Classification Method of Significant Rice Pests Based on
Deep Learning
Zhiyong Li
1,2,†
, Xueqin Jiang
1,2,†
, Xinyu Jia
1,2,†
, Xuliang Duan
1,2
, Yuchao Wang
3
and Jiong Mu
1,2,
*
1
College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
2
Sichuan Key Laboratory of Agricultural Information Engineering, Ya’an 625000, China
3
College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China
*
Correspondence: Jmu@sicau.edu.cn; Tel.: +86-133-4060-8699
†
These authors contributed equally to this work and should be regarded as co-first authors.
Abstract:
Rice pests are one of the main factors affecting rice yield. The accurate identification of pests
facilitates timely preventive measures to avoid economic losses. Some existing open source datasets
related to rice pest identification mostly include only a small number of samples, or suffer from
inter-class and intra-class variance and data imbalance challenges, which limit the application of deep
learning techniques in the field of rice pest identification. In this paper, based on the IP102 dataset, we
first reorganized a large-scale dataset for rice pest identification by Web crawler technique and manual
screening. This dataset was given the name IP_RicePests. Specifically, the dataset includes 8248 images
belonging to 14 categories. The IP_RicePests dataset was then expanded to include 14,000 images via
ARGAN data augmentation technique to address the difficulties in obtaining large samples of rice
pests. Finally, the parameters trained on the public image ImageNet dataset using VGGNet, ResNet
and MobileNet networks were used as the initial values of the target data training network to achieve
image classification in the field of rice pests. The experimental results show that all three classification
networks combined with transfer learning have good recognition accuracy, among which the highest
classification accuracy can be obtained on the IP_RicePests dataset via fine-tuning the parameters of
the VGG16 network. In addition, following ARGAN data augmentation the dataset demonstrates
high accuracy improvements in all three models, and fine-tuning the VGG16 network parameters
obtains the highest accuracy in the augmented IP_RicePests dataset. It is demonstrated that CNN
combined with transfer learning can employ the ARGAN data augmentation technique to overcome
difficulties in obtaining large sample sizes and improve the efficiency of rice pest identification. This
study provides foundational data and technical support for rice pest identification.
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