As climate change progresses, understanding connections between a crop variety’s pattern of growth and its ability to thrive in variable conditions will be crucial to maximizing agronomic output. New proximal sensing technologies, such as aerial imaging, provide a source of rich information for evaluating the genetic merit of crop varieties and breeding lines. The genetic variability of a variety’s growth patterns and its response to stress are not well understood, however. Advances are needed to translate data from aerial imaging into meaningful indicators that will enable the selection of optimal growth and development patterns across a range of environments.

With support from the Foundation for Food & Agriculture Research, a team of Cornell researchers is developing a large-scale, cost-effective platform that can model genotype-specific plant growth and development. The team, led by Kelly R. Robbins, School of Integrative Plant Science, Plant Breeding and Genetics, is working in partnership with researchers at Virginia Tech and New Mexico State, private seed companies BASF and Limagrain, and the nonprofit Virginia Crop Improvement Association. Researchers will gather a critical mass of proximal sensing data across several important crop species in contrasting environments to build the methods, models, and software necessary to leverage new data sets from proximal-sensing technologies to inform breeding decisions.

This research will facilitate the breeding and selection of new crop varieties that are most suitable in changing environments. User-friendly, open-source software created as part of this project will allow private and public breeding programs across the globe to harness the power of the new and affordable technology generated by this project.