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A core objective of the TERRA-REF project was to generate an open-accessreference dataset for the evaluation of sensing technologies to study plantsunder field conditions. The TERRA-REF program deployed a suite ofhigh-resolution, cutting edge technology sensors on a gantry system with theaim of scanning 1 hectare (10$^4$) at around 1 mm$^2$ spatial resolutionmultiple times per week. The system contains co-located sensors including astereo-pair RGB camera, a thermal imager, a laser scanner to capture 3Dstructure, and two hyperspectral cameras covering wavelengths of 300-2500nm.This sensor data is provided alongside over sixty types of traditional plantphenotype measurements that can be used to train new machine learning models.Associated weather and environmental measurements, information about agronomicmanagement and experimental design, and the genomic sequences of hundreds ofplant varieties have been collected and are available alongside the sensor andplant phenotype data.  Over the course of four years and ten growing seasons, the TERRA-REF systemgenerated over 1 PB of sensor data and almost 45 million files. The subset thathas been released to the public domain accounts for two seasons and about halfof the total data volume. This provides an unprecedented opportunity forinvestigations far beyond the core biological scope of the project.  The focus of this paper is to provide the Computer Vision and MachineLearning communities an overview of the available data and some potentialapplications of this one of a kind data.

What Does TERRA-REF's High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community?