欧亚国际

E. de Langre, O. Penalver, P. Hémon, et al.

http://doi.org/10.34133/2019/6379693

Plant Phenomics | 评估碳同位素识别值在小麦育种中的应用

evalsuating the utility of carbon isotope discrimination for wheat breeding in the pacific Northwest

Liam S. Dixon, Jayfred V. Godoy, and Arron H. Carter

http://doi.org/10.34133/2019/4528719

Plant Phenomics | 基于深度学习的识别田间条件下小麦的穗区域的表型分析方法

An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat

Tahani Alkhudaydi, Daniel Reynolds, Simon Griffiths, Ji Zhou, and Beatriz de la Iglesia

http://doi.org/10.34133/2019/7368761

Plant Phenomics | 精确表型研究揭示新的小麦叶枯病定量抗性基因位点

Precision phenotyping reveals novel loci for quantitative resistance to septoria tritici blotch

Steven Yates, Alexey Mikaberidze, Simon G. Krattinger, et al.

http://doi.org/10.34133/2019/3285904

Plant Phenomics | “获得性耐旱”性状对小麦和水稻耐旱的作用及耐旱水平的差异比较

Acquired traits contribute more to drought tolerance in wheat than in rice

Preethi Vijayaraghavareddy, Ramu S. Vemanna, Xinyou Yin, Paul C. Struik, Udayakumar Makarla, Sheshshayee Sreeman

http://doi.org/10.34133/2020/5905371

Plant Phenomics | 使用无人机多视角成像技术估算小麦幼苗分蘖数

Repeated multiview imaging for estimating seedling tiller counts of wheat genotypes using drones

Roth L, Camenzind M, Aasen H, et al.

http://doi.org/10.34133/2020/3729715

lant Phenomics | 一种基于地面激光雷达技术的小麦地上部生物量和生长率等表型特征的重复性检测方法Ground-based LiDAR improves phenotypic repeatability of above-ground biomass and crop growth rate in wheat

Deery DM, Rebetzke GJ, Jimenez-Berni JA, et al.

http://doi.org/10.34133/2020/8329798

Plant Phenomics | "Macrobot": 一种基于自动分割的白粉病定量分析系统

“Macrobot”: An automated segmentation-based system for powdery mildew disease quantification

Lück S, Strickert M, Lorbeer M, et al.

http://doi.org/10.34133/2020/5839856

Plant Phenomics | GWHD_2021：改进后的全球麦穗检测数据集

Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods

David E, Serouart M, Smith D, et al.

http://doi.org/10.34133/2021/9846158

《植物表型组学》（Plant Phenomics）是由南京农业大学和美国科学促进会（AAAS）合作创办的英文学术期刊，于2019年1月正式上线发行，是Science合作出版的第二本期刊。采用开放获取形式，刊载植物表型组学交叉学科热点领域具有突破性科研进展的原创性研究论文、综述、数据集和观点。具体范围涵盖高通量表型分析的最新技术，基于图像分析和机器学习的表型分析研究，提取表型信息的新算法，作物栽培、植物育种和农业实践中的表型组学新应用，与植物表型相结合的分子生物学、植物生理学、统计学、作物模型和其他组学研究，表型组学相关的植物生物学等。期刊已被DOAJ、Scopus、PMC、EI和SCIE等数据库收录。