Earticle

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

저자

• 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 ] Corresponding Author
• 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 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  컴퓨터게임및콘텐츠논문지(구 한국컴퓨터게임학회논문지) [Journal of Computer Games and Contents]

• 간기

  월간

• pISSN

  3091-7409

• eISSN

  3092-3638

• 수록기간

  2002~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 691 DDC 793