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 Key message Leveraging affordable red-green-blue (RGB) imaging and neural network algorithms, this study delivers a high-throughput method to quantify seasonal color shifts and genetic variation in Scots pine seedlings. Hue-saturation-brightness (HSB) color analysis, and RGB values can be used for population and seasonal differentiation and hold potential for advancing breeding programs in forestry under changing climatic conditions. Context Scots pine ( Pinus sylvestris L.) displays remarkable genetic and phenotypic diversity, with seasonal color changes such as autumn reddening, reflecting population-level responses to local environmental conditions. Advances in imaging and deep learning now enable precise quantification of such phenotypic variation, providing new insights into population-level variation. Aims This study assesses seasonal color variation within and among Scots pine seedling populations, compares the effectiveness of RGB and HSB systems for population and seasonal differentiation, and investigates phenological patterns across progenies of three seed orchards from ecologically distinct populations. Methods One-year-old seedlings from lowland (Plasy, Trebon) and upland (Decin) populations were imaged in a common garden trial in September, October, and January using a handheld camera. Needle-level segmentation was performed via a convolutional neural network. Genetic variability and population differences were analyzed using linear mixed models. Results Population differentiation reached the highest values in the RGB blue channel ( Q ST-blue = 0.64 in September and Q ST-blue = 0.94 in October) and in HSB ( Q ST-hue = 0.61, Q ST-saturation = 0.62 in September and Q ST-saturation = 0.64 in October). Color wheel visualizations revealed converging hue and saturation trajectories, indicating gradual phenological changes in the post-growing season. September values exhibited the highest heritability ( h 2 RGB = 0.12–0.25; h 2 HSB = 0.12–0.29) among measured optical traits. Conclusion This study demonstrates that RGB and HSB color parameters, extracted from high-throughput image analysis using CNN-based needle segmentation, capture both seasonal and genetic variation in Scots pine seedlings. The highest genetic differentiation and heritability occurred during early autumn, particularly in the blue and saturation parameters. These findings suggest that autumn color traits, quantifiable using simple digital imaging, can serve as cost-effective indicators in tree breeding programs.
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.
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