인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Grapevine berry chemistry varies across varieties and developmental stages and is sensitive to environmental conditions. Efforts have sought to leverage proximal remote sensing data to exploit potential covariation of foliar traits and berry composition to enable nondestructive and scalable methods for assessing berry chemistry. Traditionally, simple vegetation indices have been used based on the assumption of a relationship between foliar coverage and berry yield and composition. However, indices assessing foliar coverage, chlorophyll content, and biomass may not be sensitive enough to track subtle variations of berry chemistry across winegrape varieties and developmental stages. Thus, in this study, we seek to leverage full‐range hyperspectral data (400–2500 nm) and a partial least squares regression (PLSR) model for assessing traditional metrics of berry composition. The benefit of PLSR models for analysis of hyperspectral data is that they can integrate subtle physiological and structural changes in plant reflectance to optimize a model. In this study, we measured leaf and berry spectra and berry composition (Brix, tartaric acid, and pH) across 23 winegrape varieties and developmental stages across 2 years from June to September in a common garden in Davis, California. Our results show that both foliar and berry hyperspectral PLSR models can be used to predict berry composition across phenological stages. This suggests that proximal remote sensing of foliage has the potential to enable rapid and nondestructive monitoring of berry developmental stages to aid in management decision‐making and harvest timing.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
