Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms. Expected final online publication date for the Annual Review of Plant Biology, Volume 71 is April 29, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

中文翻译：

---

表型分析：育种者进入植物的新窗口

植物表型分析能够对植物结构和功能以及与环境的相互作用进行无创量化。高容量表型达到迄今为止难以获得的表型特征。表型的多样化、具有挑战性和有价值的应用起源于科学家、育种者和育种者，因为他们研究遗传资源的表型多样性并将日益复杂的性状应用于作物改良。非侵入性技术用于分析实验和繁殖种群。我们涵盖了种子、芽和根性状的受控环境和田间表型的最新研究。选择田间表型技术已成为最先进的技术，并有望加快早期世代的育种过程。我们重点介绍了田间植物近端和遥感快速发展背后的技术。最后，我们讨论了与表型分析界合作的新学科：数据科学，以解决生成 FAIR（可查找、可访问、可互操作和可重用）数据的挑战，以及机器人技术，将表型分析直接应用于农场。《植物生物学年度评论》第 71 卷的预计最终在线出版日期为 2020 年 4 月 29 日。请参阅 http://www.annualreviews.org/page/journal/pubdates 了解修订后的估计值。