The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.

中文翻译：

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育种者友好表型

如今，表型分析这个词可以让人联想到无人机或 phenocart 在研究地块中快速移动，收集有关各种特征的高分辨率数据集。传感器技术和数据处理的最新进展使这成为可能。尽管如此，更全面的往往具有破坏性的表型仍然可以在育种和研究中发挥很大作用。本综述考虑了三个主要领域内表型的“育种友好性”：(i)“最小数据集”，其中“方便”或易于收集和使用是最重要的，视觉评估通常是首选；(ii) 高通量表型 (HTP)，对于大多数育种者来说相对较新，与最低数据集相比，需要更大的投资，实施技术障碍和更陡峭的学习曲线；(iii) 详细的表征或“精确”表型分析，通常针对与目标环境相关的一组特征进行定制，并且需要大量的时间和资源。虽然过去一直是争论的主题，但表型分析的额外投资正变得越来越被接受，以利用作物基因组学和预测模型的最新发展，这些模型可以从高通量和详细的精确表型中建立。本综述考虑了表型分析的不同背景，包括育种、遗传资源探索、亲本构建和转化研究以提供其他新育种资源，以及上面列出的不同类别的表型如何适用于每个类别。一些相同的工具和经验法则同样适用于复杂性状的遗传分析和基因发现的表型分析。