Webinar: Get Grounded in TERRA-REF: Publicly-accessible, Hi-res Sensor Data for Crop Phenomics

March 13, 2020 | Virtual

10am Pacific | 11am Mountain | 12noon Central | 1pm Eastern (Daylight Savings Time)

Webinar Materials:

Webinar Video-recording:

About this Webinar: 

This webinar will introduce the TERRA-REF project that produced a large, high resolution public domain dataset to advance high throughput plant phenomics. The TERRA REF dataset contains remote sensing, plant trait, environmental, agronomic and genomic data. The dataset was produced to enable researchers to evaluate and develop new computational pipelines for converting sensor data into biological and agronomic understanding and solutions. David LeBauer, who led development of TERRA-REF's data and computing pipeline will describe the project along with its computing infrastructure and datasets. He will showcase the diverse suite of data generated by thermal, hyperspectral, color, laser 3D, and active photosynthetic fluorescence cameras as well as hand measurements, environmental sensors, and deep genomic sequencing.

Anyone interested in plant sciences, robotics, statistics, remote sensing, data science, and how to make such data FAIR (findable, accessible, interoperable, and reusable) will benefit from learning about TERRA-REF's publicly-funded and publicly-accessible crop phenotype datasets. David will describe how the TERRA REF data was produced and how users can learn more and get started using the data and contributing new algorithms to high throughput phenomics pipelines.

About the Presenter:

David LeBauer

David LeBauer is the director of Data Science for the Agricultural Experiment Station at the University of Arizona. His research is focused on using science to engineer more sustainable and productive crops and agricultural systems. To support this effort, he develops open software and data to integrate data and knowledge across disciplines. Key projects include the Predictive Ecosystem Analyzer (PEcAn) framework for data synthesis and forecasting with crop and ecosystem models and the TERRA Reference phenotyping database and computing pipeline.