Attention Enhanced GoogLeNet for Crop Leaf Disease Classification

• 발행기관: 한국컴퓨터게임학회
• 저자명: 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
• 간행물 정보: 『한국컴퓨터게임학회논문지』제38권 1호, 98~105쪽, 전체 8쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2025.03.31

국문 초록

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

1. Introduction

2. Related Work

3. Materials and Methods

4. Results and Discussion

5. Conclusion and Future Work

참고문헌

키워드

Precision Agriculture Crop Disease Classification Deep Learning GoogLeNet Attention Mechanism