Malia A. Gehan

Lights, camera, action: high-throughput plant phenotyping is ready for a close-up

Anticipated population growth, shifting demographics, and environmental variability over the next century are expected to threaten global food security. In the face of these challenges, crop yield for food and fuel must be maintained and improved using fewer input resources. In recent years, genetic tools for profiling crop germplasm has benefited from rapid advances in DNA sequencing, and now similar advances are needed to improve the throughput of plant phenotyping. We highlight recent developments in high-throughput plant phenotyping using robotic-assisted imaging platforms and computer vision-assisted analysis tools.

A Versatile Phenotyping System and Analytics Platform Reveals Diverse Temporal Responses to Water Availability in Setaria.

Phenotyping has become the rate-limiting step in using large-scale genomic data to understand and improve agricultural crops. Here, the Bellwether Phenotyping Platform for controlled-environment plant growth and automated multimodal phenotyping is described. The system has capacity for 1140 plants, which pass daily through stations to record fluorescence, near-infrared, and visible images. Plant Computer Vision (PlantCV) was developed as open-source, hardware platform-independent software for quantitative image analysis. In a 4-week experiment, wild Setaria viridis and domesticated Setaria italica had fundamentally different temporal responses to water availability. While both lines produced similar levels of biomass under limited water conditions, Setaria viridis maintained the same water-use efficiency under water replete conditions, while Setaria italica shifted to less efficient growth. Overall, the Bellwether Phenotyping Platform and PlantCV software detected significant effects of genotype and environment on height, biomass, water-use efficiency, color, plant architecture, and tissue water status traits. All ∼ 79,000 images acquired during the course of the experiment are publicly available.

Transcriptional networks — crops, clocks, and abiotic stress

Several factors affect the yield potential and geographical range of crops including the circadian clock, water availability, and seasonal temperature changes. In order to sustain and increase plant productivity on marginal land in the face of both biotic and abiotic stresses, we need to more efficiently generate stress-resistant crops through marker-assisted breeding, genetic modification, and new genome-editing technologies. To leverage these strategies for producing the next generation of crops, future transcriptomic data acquisition should be pursued with an appropriate temporal design and analyzed with a network-centric approach. The following review focuses on recent developments in abiotic stress transcriptional networks in economically important crops and will highlight the utility of correlation-based network analysis and applications.

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics.

PlantCV v2: Image analysis software for high-throughput plant phenotyping

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

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.

Raspberry Pi-powered imaging for plant phenotyping

Premise of the Study Image‐based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost‐prohibitive. To make high‐throughput phenotyping methods more accessible, low‐cost microcomputers and cameras can be used to acquire plant image data. Methods and Results We used low‐cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi–controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open‐source image processing software such as PlantCV. Conclusions This protocol describes three low‐cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open‐source image processing tools, these imaging platforms provide viable low‐cost solutions for incorporating high‐throughput phenomics into a wide range of research programs.

Temporal network analysis identifies early physiological and transcriptomic indicators of mild drought in Brassica rapa

The dynamics of local climates make development of agricultural strategies challenging. Yield improvement has progressed slowly, especially in drought-prone regions where annual crop production suffers from episodic aridity. Underlying drought responses are circadian and diel control of gene expression that regulate daily variations in metabolic and physiological pathways. To identify transcriptomic changes that occur in the crop Brassica rapa during initial perception of drought, we applied a co-expression network approach to associate rhythmic gene expression changes with physiological responses. Coupled analysis of transcriptome and physiological parameters over a two-day time course in control and drought-stressed plants provided temporal resolution necessary for correlation of network modules with dynamic changes in stomatal conductance, photosynthetic rate, and photosystem II efficiency. This approach enabled the identification of drought-responsive genes based on their differential rhythmic expression profiles in well-watered versus droughted networks and provided new insights into the dynamic physiological changes that occur during drought.

The Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology

Training for experimental plant biologists needs to combine bioinformatics, quantitative approaches, computational biology, and training in the art of collaboration, best achieved through fully integrated curriculum development.

Classifying cold stress responses of inbred maize seedlings using RGB imaging

Increasing the tolerance of maize seedlings to low temperature episodes could mitigate the effects of increasing climate variability on yield. To aid progress toward this goal, we established a growth chamber-based system for subjecting seedlings of 40 maize inbred genotypes to a defined, temporary cold stress while collecting digital profile images over a 9-day time course. Image analysis performed with PlantCV software quantified shoot height, shoot area, 14 other morphological traits, and necrosis identified by color analysis. Hierarchical clustering of changes in growth rates of morphological traits and quantification of leaf necrosis over two time intervals resulted in three clusters of genotypes, which are characterized by unique responses to cold stress. For any given genotype, the set of traits with similar growth rates is unique. However, the patterns among traits are different between genotypes. Cold sensitivity was not correlated with the latitude where the inbred varieties were released suggesting potential further improvement for this trait. This work will serve as the basis for future experiments investigating the genetic basis of recovery to cold stress in maize seedlings.