한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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고추 작물의 정밀 질병 진단을 위한 딥러닝 모델 통합 연구: YOLOv8, ResNet50, Faster R-CNN의 성능 분석

Integrated Deep Learning Models for Precise Disease Diagnosis in Pepper Crops: Performance Analysis of YOLOv8, ResNet50, and Faster R-CNN

  • 서지인 (순천대학교 스마트농업전공) ;
  • 심현 (국립순천대학교 스마트농업전공학과)
  • 투고 : 2024.06.30
  • 심사 : 2024.07.25
  • 발행 : 2024.08.31
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초록

본 연구의 목적은 YOLOv8, ResNet50, Faster R-CNN 모델을 활용하여 고추 작물의 질병을 진단하고, 각 모델의 성능을 비교하는 것이다. 첫 번째 모델은 YOLOv8을 사용하여 질병을 진단하였고, 두 번째 모델은 ResNet50을 단독으로 사용하였다. 세 번째 모델은 YOLOv8과 ResNet50을 결합하여 질병을 진단하였으며, 네 번째 모델은 Faster R-CNN을 사용하여 질병을 진단하였다. 각 모델의 성능은 정확도, 정밀도, 재현율, F1-Score 지표로 평가된다. 연구 결과, YOLOv8과 ResNet50을 결합한 모델이 가장 높은 성능을 보였으며, YOLOv8 단독 모델도 높은 성능을 나타냈다.

The purpose of this study is to diagnose diseases in pepper crops using YOLOv8, ResNet50, and Faster R-CNN models and compare their performance. The first model utilizes YOLOv8 for disease diagnosis, the second model uses ResNet50 alone, the third model combines YOLOv8 and ResNet50, and the fourth model uses Faster R-CNN. The performance of each model was evaluated using metrics such as accuracy, precision, recall, and F1-Score. The results show that the combined YOLOv8 and ResNet50 model achieved the highest performance, while the YOLOv8 standalone model also demonstrated high performance.

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과제정보

본 논문은 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 지역지능화혁신인재양성사업임(IITP-2024-2020-0-01489).

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