High-throughput phenotyping.

Anyone who has written a species description knows the slow process of measuring the length and width of plant parts with a ruler and ocular micrometer, counting hairs or branches, or assessing the color of fruits. Anyone who has studied plant communities has counted seedlings, measured leaf area, or laid out plots and counted their contents. Until recently, however, optimizing the speed of the process has not been a high priority. If it takes an hour to measure one herbarium specimen, how might that be reduced to minutes? If it takes 10 undergraduates a week to record plant communities along a transect, how might one undergraduate accomplish the same work in an aft ernoon? Lower-cost, automated and semiautomated methods for data acquisition and analysis are now being developed, enabled by inexpensive cameras and computers with open-source soft ware. Most recent applications have been in crops and model organisms, but the tools can be extended to systematics and ecology, fi elds that oft en require huge amounts of specimen data. In this essay we describe a few available tools to encourage readers to consider ways to increase the throughput of their own research. While the term high-throughput phenotyping could apply to any morphological, physiological, or biochemical phenotype, here we focus on morphology or other phenotypes (e.g., drought response) that can be captured using images. “High throughput” was defined by Fahlgren et al. (2015a) as “hundreds of plants per day”, but for many projects even tens of plants per day would be a massive leap forward.