To ensure the food, fiber and energy supply for a global population of 9.7 billion people by 2050, and increase biosecurity and social stability under projected climate change scenarios, we need to create crop plants that deliver more nutritional value and higher yields while requiring lower inputs (e.g. water and nutrient) and can resist local environmental challenges. To this end, plant scientists are developing and adopting cutting-edge advances in plant biology to establish novel traits in crop plants; however, greater knowledge of factors that influence crop growth and development is needed to improve plant breeding development pipelines.

During recent decades, there has been a tremendous increase in throughput and decrease in cost of genome sequencing (Shendure and Ji, 2008). As a result of advancements in next generation sequencing (NGS) technologies, many biological fields including plant breeding and genetics have been revolutionized. Plant breeders can use the high throughput data generated from NGS to genotype mapping populations for gene or quantitative trait loci (QTL) discovery as well as predict and select desired individuals based on their genome estimated breeding values (Varshney et al., 2014). Phenotyping, however, is a key requirement for successful implementation of molecular mapping and breeding strategies including linkage mapping, genome-wide association mapping (GWAS), marker assisted selection (MAS), and genomic selection. The level of throughput and data dimension obtained in estimation of phenotype has lagged behind that of genotype (White et al., 2012; Araus and Cairns, 2014). The challenge has shifted from understanding the genotype to understanding the phenotype – characteristics of a plant that are determined by the effects of genetic background, production environment, and management factors such as irrigation and fertilizer treatments. 

Tackling the Phenotyping Bottleneck

More efficient plant breeding methodologies are needed to increase crop productivity to feed an ever-growing world population (Tester and Langridge, 2010). Most research programs are still relying on manual or semi-automated systems for collecting plant phenotype data resulting in high costs and low-throughput (White et al., 2012; Araus and Cairns, 2014). Low-throughput phenotyping is time-consuming and laborious, which often forces breeding programs to limit selection to yield evaluated in multi-environment, multi-year trials; even though the heritability of yield is among the lowest of commonly evaluated traits (Furbank and Tester, 2011).

Plant breeders and geneticists are interested in increasing the throughput of phenotyping at each stage of their breeding programs. Increased throughput is required because the typical breeding programs screen thousands of individuals at different stages of development. However, major changes have not been made in the phenotyping methodology over the years (Cooper et al., 2014). A relatively new approach to phenotyping, based on remote and proximal sensing, also known as High Throughput Phenotyping (HTP) or Phenomics in the Plant Science literature, may provide new tools for tackling the phenotyping bottleneck in plant breeding (Furbank and Tester, 2011; Araus and Cairns, 2014). HTP can increase the genetic gain by increasing selection intensity, phenotype repeatability, and trait heritability (Araus et al., 2018). Selection intensity is a function of the number of lines selected compared to the number of lines evaluated. With HTP, larger populations can be evaluated and more stringent selection criteria can be imposed. Responses to selection can be increased by minimizing the non-genetic variance through increasing trait repeatability and heritability (Bernardo, 2014). HTP also allows for increased replication and reduced between-measurement error by removing the human subjectivity in phenotyping. However, more work is needed to develop and optimize these systems.

High-Throughput Phenotyping Requires a Convergence of Technology in Agriculture

Phenomics is a key link between progress in plant genomics and novel trait development in agronomic crops because of its focus on assessing and quantifying plant traits at multiple scales. The phenotype of an organism refers to the observable morphological and physiological properties of the organism. Yet, plant phenotyping has been a bottleneck that limits efficient adoption of genomic tools to improve crop breeding.  Until recently, the majority of plant phenotyping data have been manually collected and were low throughput, time-consuming, and labor intensive. Consequently, these traits were often limited to specific growth stages and did not reflect the dynamic response of crop plants to variations in environmental conditions. Recent advances in sensing technology, machine learning and computer vision technologies offer the opportunity of high throughput measurements of intricate morphological and biophysical traits of plants, providing us the capabilities to ascertain plant response to environmental variation both in controlled and field environments. These advancements empower plant biologists to acquire extensive information on key plant traits, therefore are of paramount importance in the quest to address current and emerging issues related to food security, link phenomics to underlying genes and gene networks, optimize yields, achieve resource use efficiencies (e.g., water, nutrient, and light), understand resistance to biotic and abiotic stresses, and develop biomass for bioenergy and other valuable traits in plants (Das Choudhury et al., 2020). 

Remote sensing has been utilized to generate agricultural data for a number of years in a variety of ways.  Remote sensing is defined as the field of deriving data from earth’s land and water surfaces using overhead images that are produced using reflected or emitted light from some region of the electromagnetic spectrum (Campbell and Wynne, 2011).  Recent technological advancements in remote and proximal sensing have made it possible to extract massive volumes of morphological, physiological, and agronomic data from crops, but complexities in data processing, feature extraction, and data analytics make predictions of crop performance from remote sensing data a challenge. Multi-Sensor systems have been integrated on air-borne platforms for mobile mapping of agricultural fields (El-bahnasawy et al., 2018) and methods for spatial and temporal calibration have been developed (Habib et al., 2016; Habib et al., 2017; Ravi et al., 2018). Analytical tools for image and data processing, compression and integration have also been developed (Zhang et al., 2016; He et al., 2018; Ribera et al., 2018).  Complete pipelines for measuring and predicting plant productivity and performance from multi-modal data, and linking all the way to changes in genotypes are being developed by different institutions and for diverse crops (Chen et al., 2017, 2018; Masjedi et al., 2020). High-throughput controlled environment phenotyping pipelines that include robotic, environment controlled, fully automated systems have been developed for greenhouses and growth chambers (Cotrozzi et al., 2020). Additional plant phenotyping pipelines for measuring and predicting plant productivity and performance from remotely sensed data are needed. These may include both ground-based and airborne sensor platforms and sensing apparatus for high-throughput phenotyping.

We propose the creation of a North Central regional plant phenotyping research project to promote advancements in phenotyping related research. This project will leverage established plant phenotyping facilities and capacities in research institutions in the North Central region and nurture research collaborations among scientists and engineers. Creation of this committee will coordinate multidisciplinary teams of scientists and engineers in the development of high-throughput phenotyping (HTP) facilities and systems in controlled and field environments. These systems will enable researchers to overcome the phenotyping bottleneck in plant science and crop improvement programs, thereby expanding our knowledge concerning the connection between genomics and predictive phenomics of key US crops at different sites across the region. This proposed project is completely aligned with USDA NIFA’s priorities for research to enhance food security and sustainable bioenergy through improved adaptation to climate variability and change.

The synergy of the regional HTP facilities across campuses will provide scientists and engineer unprecedented capability in conducting multi-site, hypothesis-based research. This science will characterize the impacts of biotic and abiotic environment, and production systems on the growth, productivity and resilience of various crops, hence providing greater accuracy in predicting crop performance under variable conditions. This committee will also catalyze and coordinate interdisciplinary research to facilitate the creation of novel knowledge of the impact of genome and environment interaction on plant phenotype, which eventually could result in increased productivity and sustainability of our agro-ecosystems. These efforts to develop complete pipelines for measuring and predicting plant productivity and performance from multi-modal data, and linking all the way to changes in genotypes of individual plants, is unprecedented, and is key for both mitigating climate change and sustainable agriculture intensification.