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Attention Enhanced GoogLeNet for Crop Leaf Disease Classification

원문정보

Seung-Eon Jeong, Changyu AO, Soo-Kyung Moon, Dae-Won Park, Youn-Mo Soung, Man-Sung Kwen, Uk Cho, Dae-In Kang, Sung-Ho Jung, Gwang-Jun Kim

한국컴퓨터게임학회 한국컴퓨터게임학회논문지 제38권 제1호 2025.03 pp.98-105
피인용수 : 0건 (자료제공 : 네이버학술정보)

초록

영어

Crop diseases seriously affect food security, and traditional identification methods are inefficient and inaccurate. This paper proposes a GoogLeNet model with an attention mechanism. By integrating an attention module inside the Inception module, the recognition ability of subtle disease features and complex backgrounds is improved. Based on strict data preprocessing and enhancement, the proposed method achieves 87.75% accuracy on the AI Challenger 2018 crop disease dataset, which is better than the existing advanced methods, which verifies the effectiveness and practicability of the method and provides technical support for smart agriculture.

목차

ABSTRACT
1. Introduction
2. Related Work
3. Materials and Methods
3.1 Datasets and Preprocessing
3.2 GoogLeNet and Inception Module
3.3 Attention Mechanism Module
3.4 Proposed Fusion Strategies
4. Results and Discussion
4.1 Experiments Settings
4.2 Comparison with the baseline model
4.3 Comparison with state-of-the-art methods
5. Conclusion and Future Work
참고문헌

키워드

저자정보

  • Seung-Eon Jeong Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea
  • Changyu AO Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea
  • Soo-Kyung Moon Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea
  • Dae-Won Park Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea
  • Youn-Mo Soung Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea
  • Man-Sung Kwen Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea
  • Uk Cho Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea
  • Dae-In Kang Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea
  • Sung-Ho Jung Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea
  • Gwang-Jun Kim Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea

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