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

ABSTRACT
1. Introduction
2. Related Work
3. Materials and Methods
4. Results and Discussion
5. Conclusion And Future Work
참고문헌
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• Soo-Kyung Moon(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 문수경
• Changyu-AO(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 아오창유 Corresponding author
• Seung-Eon Jeong(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 정승언
• Dae-Won Park(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 박대원
• Youn-Mo Soung(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 성윤모
• Man-Sung Kwen(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 권만성
• Uk Cho(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 조욱
• Dae-In Kang(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 강대인
• Sung-Ho Jung(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 정성호
• Gwang-Jun Kim(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 김광준