Classification of Typical Pests and Diseases of Rice Based on the ECA Attention Mechanism

Hongjun Ni, Zhiwei Shi, Stephen Karungaru, Shuaishuai Lv (), Xiaoyuan Li, Xingxing Wang and Jiaqiao Zhang
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Hongjun Ni: School of Mechanical Engineering, Nantong University, Nantong 226019, China
Zhiwei Shi: School of Mechanical Engineering, Nantong University, Nantong 226019, China
Stephen Karungaru: Graduate School of Advanced Technology and Science, Tokushima University, Tokushima 770-8506, Japan
Shuaishuai Lv: School of Mechanical Engineering, Nantong University, Nantong 226019, China
Xiaoyuan Li: School of Mechanical Engineering, Nantong University, Nantong 226019, China
Xingxing Wang: School of Mechanical Engineering, Nantong University, Nantong 226019, China
Jiaqiao Zhang: School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Agriculture, 2023, vol. 13, issue 5, 1-15

Abstract: Rice, a staple food crop worldwide, is pivotal in agricultural productivity and public health. Automatic classification of typical rice pests and diseases is crucial for optimizing rice yield and quality in practical production. However, infrequent occurrences of specific pests and diseases lead to uneven dataset samples and similar early-stage symptoms, posing challenges for effective identification methods. In this study, we employ four image enhancement techniques—flipping, modifying saturation, modifying contrast, and adding blur—to balance dataset samples throughout the classification process. Simultaneously, we enhance the basic RepVGG model by incorporating the ECA attention mechanism within the Block and after the Head, resulting in the proposal of a new classification model, RepVGG_ECA . The model successfully classifies six categories: five types of typical pests and diseases, along with healthy rice plants, achieving a classification accuracy of 97.06%, outperforming ResNet34 , ResNeXt50 , Shufflenet V2 , and the basic RepVGG by 1.85%, 1.18%, 3.39%, and 1.09%, respectively. Furthermore, the ablation study demonstrates that optimal classification results are attained by integrating the ECA attention mechanism after the Head and within the Block of RepVGG . As a result, the classification method presented in this study provides a valuable reference for identifying typical rice pests and diseases.

Keywords: rice; pest and disease classification; ECA; attention mechanism; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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