The overall purpose of this project is to improve breeding of plant crops by using modern remote sensing and high throughput phenotyping methods to gather important information about plant traits. We collect high resolution phenomic data and make these publicly available, which can be combined with weather and genomic data to produce insights about plant structure and function. We also aim to provide the tools and training needed to effectively leverage TERRA REF data. Visit terraref.org for more information.
- Burnette M, Kooper R, Maloney JD, Rohde GS, Terstriep JA, Willis C, Fahlgren N, Mockler T, Newcomb M, Sagan V, Andrade-Sanchez P. TERRA-REF data processing infrastructure. InProceedings of the Practice and Experience on Advanced Research Computing 2018 Jul 22 (p. 27). DOI:10.1145/3219104.3219152. (pdf)
- LeBauer, David et al. (2020), Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics, and imaging sensors, Dryad, Dataset, DOI:10.5061/dryad.4b8gtht99
- LeBauer, D., Burnette, M., Fahlgren, N., Kooper, R., McHenry, K., & Stylianou, A. (2021). What Does TERRA-REF's High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community? ICCV 2021 7th workshop on Computer Vision in Plant Phenotyping and Agriculture. DOI:arXiv:2107.14072.
- More on the TERRA-REF publications page
The Predictive Ecosystem Analyzer (PEcAn) is a framework for running ecosystem models. Our mission is to "Develop and promote accessible tools for reproducible ecosystem modeling and forecasting". It provides a workflow and standard inputs and outputs to support modeling, as well as a Bayesian approach to integrate information contained in biophysical models and data across multiple sources and scales.
We are currently using PEcAn to predict the physiology, growth, and ecology of genetically modified crops before they can be grown in field trials. Previously, we have used PEcAn to predict the productivity and yield stability of bioenergy crops at regional to global scales.
- LeBauer DS, Wang D, Richter KT, Davidson CC, Dietze MC. Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs. 2013 May;83(2):133-54. DOI:10.1890/12-0137.1. (pdf)
- Dietze MC, LeBauer DS, Kooper RO. On improving the communication between models and data. Plant, Cell & Environment. 2013 Sep;36(9):1575-85. DOI:10.1111/pce.12043. (pdf)
- Additional key publications
- PEcAn Google Scholar page
The goals motivating the drone pipeline are (in no particular order):
- common processing: provide components that are reusable in multiple environments
- dynamic work flows: mix common and unique processing components to create meaningful processing pipelines
- scalable work flows: use scalable architecture as needed, in the right places, to return results faster
The drone pipeline effort is derived from the larger TERRA REF project, and we are working to adapt these tools to facilitate the use of drones and other platforms for agricultural research. Additional code and information can be found on our project page.
- Schnaufer, Christophe, Julian L. Pistorius, and David S. LeBauer. "An open, scalable, and flexible framework for automated aerial measurement of field experiments." Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V. Vol. 11414. International Society for Optics and Photonics, 2020. DOI:10.1117/12.2560008. (pdf)
The purpose of the Sentinel project is to design plants to sense and detect biological compounds. We are using a grass species that will have varying traits, including height and fluorescence, in response to exposure to certain compounds.
The purpose of this project is to increase the findability and reusability of agricultural data by creating an Agricultural Research Data Network (ARDN). Specifically, this was a cross-institutional collaborative effort to convert a set of pre-existing datasets to the AgMIP Crop Experiment (ACE) crop model format, including the TERRA-REF dataset, to enable these to be combined for crop modeling. Resulting datasets are published openly on the National Agricultural Library on USDA Ag Data Commons. We also included templates for converting from BrAPI and other formats to enable other future or similar datasets to be more easily converted into formats that can be used as inputs to crop models.
Because we are using the Breeder's API (BrAPI) version of the TERRA-REF dataset for this, we want to enable other BrAPI-compatible datasets to be convertible. In addition to publishing TERRA REF data and converters at the National Agricultural Library, we contributed to the design and implementation of a new events endpoint in the BrAPI v1.3 specification that provides a method for providing agronomic and experimental metadata alongside data from breeding trials.
- Cheryl H. Porter, Chris Villalobos, Dean Holzworth, Roger Nelson, Jeffrey W. White, Ioannis N. Athanasiadis, Sander Janssen, Dominique Ripoche, Julien Cufi, Dirk Raes, Meng Zhang, Rob Knapen, Ritvik Sahajpal, Kenneth Boote, James W. Jones. Harmonization and translation of crop modeling data to ensure interoperability. Environmental Modelling & Software. 2014;62:495-508. DOI:10.1016/j.envsoft.2014.09.004
Predicting plant phenotypes using machine learning models and environmental and genomic data. Our role is to curate phenotypic and meteorological data, including TERRA REF, National Ecological Observatory Network phenology data, and National Phenology Network data, to be usable and well-documented by collaborators.