YOLOv8 딥러닝 모델을 통한 새싹 채소의 부위별 길이 측정

• 영문명: Application of Deep Learning Technology for Phenotyping Tissue Specific Length of Sprout Vegetables Using YOLOv8
• 발행기관: 한국육종학회
• 저자명: 조영훈(Yeonghun Cho) 김재윤(Jae Yoon Kim) 하정민(Jungmin Ha)
• 간행물 정보: 『한국육종학회지』Vol.56 No.4, 417~424쪽, 전체 8쪽
• 주제분류: 농수해양 > 기타농수해양
• 파일형태: PDF
• 발행일자: 2024.12.01

국문 초록

Deep learning has gained considerable interest in agricultural breeding research. While advances in sequencing technologies have made genotypic data collection easier in genomic breeding, phenotypic data collection remains labor intensive and time consuming. Furthermore, as traditional phenotypic data collection relies heavily on manual processes, the results may vary based on the researcher’s skill and criteria. Thus, automated phenotypic data collection is essential for addressing these challenges. In this study, we aimed to develop a deep learning model using the YOLOv8 framework to measure the lengths of hypocotyls and roots in sprout vegetables such as mung bean, cowpea, and soybean. Our model automates the measurement process, accurately identifies the hypocotyl and root using Roboflow, and subsequently measures their lengths with high precision in various legume species. This approach addresses the challenges of extensive phenotypic data collection, which is essential for genetic breeding and agricultural improvement. Our deep learning model facilitates consistent and accurate data collection in large-scale studies by controlling variables influenced by the researcher’s skills and criteria. This reduces errors and enhances data reliability and accuracy, which are crucial for successful breeding practices and agricultural research.

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키워드

sprout vegetable deep learning phenotyping YOLO

참고문헌

• Plant Breed Biotech
• Int J Food Sci
• Korean J Plant Res
• Korean J Breed Sci
• Plant Breed Biotech
• Korean J Breed Sci
• J Korea Soc Food Sci Nutr
• Plants
• Smart Media Journal
• Comput Electron Agric
• Korean J Agric Sci
• Front Plant Sci
• J Food Qual
• J Sci Food Agric
• Curr Opin Chem Biol
• In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
• Plant Methods
• Int J Food Sci