TERRA-REF - field scanner

TERRA REF inspired artwork!

dlebauer — Fri, 17 Dec 2021 23:21:06 +0000

Dec. 17, 2021

Created by Chen Wang at NCSA

TERRA REF has enabled advances in research and engineering.

Now, we can add art to the list. A new series of artwork by inspired by Chen Wang inspired by a variety of projects at the National Center for Supercomputing Applications includes one based on the TERRA REF gantry, and a future with robots in the field. See this and other art in the flickr album and follow her @chenniferwang on Instagram.

Multi-Resolution Outlier Pooling for Sorghum Classification

dlebauer — Wed, 15 Sep 2021 17:38:31 +0000

field scanner

reference data

sensor data

algorithms

June 19, 2021

This paper introduces the Sorghum-100 dataset, a large dataset of RGB imagery captured by the TERRA-REF field scanner as well as a new and more powerful approach to identifying which plant variety (cultivar) is in a particular image.

The Sorghum-100 dataset was used in the Sorghum-100 Kaggle competition, which explains:

The Sorghum-100 dataset is a curated subset of the RGB imagery captured during the TERRA-REF experiments, labeled by cultivar. This data could be used to develop and assess a variety of plant phenotyping models which seek to answer questions relating to the presence or absence of desirable traits (e.g., "does this plant exhibit signs of water stress?''). In this contest, we focus on the question: ``What cultivar is shown in this image?''

Chao Ren, Justin Dulay, Gregory Rolwes, Duke Pauli, Nadia Shakoor, Abby Stylianou

Ren_Multi-Resolution_Outlier_Pooling_for_Sorghum_Classification_CVPRW_2021_paper.pdf

Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (Sorghum bicolor) in a field setting

dlebauer — Wed, 15 Sep 2021 17:28:24 +0000

trait data

field scanner

reference data

sensor data

algorithms

Aug. 10, 2020

Herritt et al 2020 demonstrate the PSII camera's ability to capture PSII fluorescence by treating leaves with a chemical that inhibits photosynthesis (DMCU) and observing differences.

Herritt, M.T., Pauli, D., Mockler, T.C. et al. Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (Sorghum bicolor) in a field setting. Plant Methods 16, 109 (2020). https://doi.org/10.1186/s13007-020-00650-0

Herritt, Matthew T., Duke Pauli, Todd C. Mockler, and Alison L. Thompson

Herritt2020_Article_ChlorophyllFluorescenceImaging.pdf

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

dlebauer — Wed, 18 Aug 2021 17:57:52 +0000

field scanner

reference data

software

sensor data

algorithms

July 15, 2021

This paper describes the pipeline used to produce calibrated hyperspectral images from the two hyperspectral cameras deployed under field conditions. The pipeline implements radiometric calibration, reflectance calculation, bidirectional reflectance distribution function (BRDF) correction, soil and shadow masking, and image quality assessment. This was a true tour de force!

Calibrating these cameras was one of the project's most challenging efforts - the light environment is changing, and impacted not only by the sun but also by the big white box that both reflects and shades, the complex canopy, the slow moving camera and timing of calibration, not to mention broken cameras and the variety of radiometers and calibration targets that were put to use. The issues on GitHub include an interesting record of the discussions, challenges, and iterations of different approaches that we took over the years.

Sagan, Vasit, et al. "Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data." IEEE Transactions on Geoscience and Remote Sensing (2021).

Vasit Sagan, Maitiniyazi Maimaitijiang, Sidike Paheding, Sourav Bhadra, Nichole Gosselin, Max Burnette, Jeffrey Demieville, Sean Hrtling, David LeBauer, Maria Newcomb, Duke Paul, Kyle Peterson, Nadia Shakoor, Abby Stylianou, Charles Zender, Todd Mockler

sagan2021dda_small.pdf

TERRA-REF Data Processing Infrastructure

dlebauer — Thu, 31 Jan 2019 16:35:16 +0000

trait data

standards

field scanner

computing pipeline

analysis workbench

reference data

software

sensor data

algorithms

July 22, 2018

The Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) provides a data and computation pipeline responsible for collecting, transferring, processing and distributing large volumes of crop sensing and genomic data from genetically informative germplasm sets. The primary source of these data is a field scanner system built over an experimental field at the University of Arizona Maricopa Agricultural Center. The scanner uses several different sensors to observe the field at a dense collection frequency with high resolution. These sensors include RGB stereo, thermal, pulse-amplitude modulated chlorophyll fluorescence, imaging spectrometer cameras, a 3D laser scanner, and environmental monitors. In addition, data from sensors mounted on tractors, UAVs, an indoor controlled-environment facility, and manually collected measurements are integrated into the pipeline. Upt to two TB of data per day are collected and transferred to NCSA at the University of Illinois where they are processed.

In this paper we describe the technical architecture for the TERRA-REF data and computing pipeline. This modular and scalable pipeline provides a suite of components to convert raw imagery to standard formats, geospatially subset data, and identify biophysical and physiological plant features related to crop productivity, resource use, and stress tolerance. Derived data products are uploaded to the Clowder content management system and the BETYdb traits and yields database for querying, supporting research at an experimental plot level. All software is open source under a BSD 3-clause or similar license and the data products are open access (currently for evaluation with a full release in fall 2019). In addition, we provide computing environments in which users can explore data and develop new tools. The goal of this system is to enable scientists to evaluate and use data, create new algorithms, and advance the science of digital agriculture and crop improvement.

Burnette, Willis, Kooper, Maloney, Ward, Shakoor, Newcomb, Rohde, Fahlgren, Sagan, Sidike, Terstriep, LeBauer

Burnette et al 2018 Article

World's Largest Ag Robot Video

hfournier — Wed, 31 Oct 2018 23:09:11 +0000

field scanner

April 21, 2016

David LeBauer, ARPA-E Terra-Ref Data and Computing Team

Lemnatec Field Scanalyzer

The Lemnatec Field Scanalyzer at the USDA Arid Land Research Station in Maricopa, Arizona is the largest field crop analytics robot in the world (to our knowledge). This high-throughput phenotyping field-scanning robot has a 30-ton steel gantry that autonomously moves along two 200-meter steel rails while continuously imaging the crops growing below it with a diverse array of cameras and sensors. It was constructed in the winter of 2015-2016, the first data was collected in February and the first Sorghum trials begin in April 2016. We aim to make this unprecedented data stream as open and accessible as possible.

Time Lapse Video

This time-lapse video that compresses the live video feed collected between October 29, 2015 and February 1 2016.

TERRA-REF - field scanner

TERRA REF inspired artwork!

Multi-Resolution Outlier Pooling for Sorghum Classification

Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (Sorghum bicolor) in a field setting

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

TERRA-REF Data Processing Infrastructure

World's Largest Ag Robot Video

Lemnatec Field Scanalyzer

Time Lapse Video

TERRA-REF Field Scanalyzer Assembly at UA-MAC