Earticle

초록

영어

Germination of chili pepper seeds is critical for crop yield and resource utilization. A high germination rate increases yield and effectively reduces resource wastage. This study collected 450 macroscopic images of chili pepper seeds and constructed a dataset for deep learning training through standardized germination experiments. Six deep learning models were evaluated to improve the chili pepper seed classification accuracy and germination rate. After comparing the performance of the models, MobileNet_v2 performed the best, not only having the fewest number of parameters but also achieving a 98.89% accuracy and 97.82% F1 score. The model improved the original germination rate from 87.33% to 100% on the test set, significantly optimizing the seed selection process

목차

ABSTRACT
1. Introduction
2. Related Work
3. Materials and Methods
4. Results and Discussion
5. Conclusion And Future Work
참고문헌

키워드

저자

• Soo-Kyung Moon [ Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Kore ]
• Changyu-Ao [ Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea ] Corresponding Author
• Seung-Eon Jeong [ 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