Harnessing the Ag Data Revolution for Modeling Plant and Agronomic Systems Across Scale

The next revolution in precision agriculture solutions will require an improved understanding of the complex regulatory mechanisms that are instrumental in plant growth, development, and adaptation.  Key in these efforts is the ability to acquire and analyze data across biological scales (from molecular to phenotypic scales).  High-throughput data that have been collected across biological scales include molecular data such as gene expression profiles and confocal imaging to data capturing plant physiology such as hyperspectral imaging and remote sensing.  The diversity of these datasets (in combination with the complexity of plant systems) has created opportunities to develop novel computational intelligence and machine-learning approaches that are capable of modeling plant systems within and across biological scales.

In this presentation, Dr. Williams will provide a brief overview of approaches for analyzing various types of high-throughput biological data.  These approaches address the many challenges associated with analyzing biological data, including the need to mitigate high variation and/or uncertainty in data, the need for novel segmentation and feature extraction, and the integration of disparate datasets for making causal inferences across scale.  He will also discuss steps his team is taking as part of the N.C. Plant Sciences Initiative to accelerate data-driven plant sciences for large-scale interdisciplinary projects.  The continued acquisition of high-throughput data across scale and the continued development of novel machine learning and modeling tools will provide opportunities to further push the boundaries of our understanding of plant systems and will be key to a better understanding of how plants respond to complex environments.