  A core objective of the TERRA-REF project was to generate an open-access
reference dataset for the evaluation of sensing technologies to study plants
under field conditions. The TERRA-REF program deployed a suite of
high-resolution, cutting edge technology sensors on a gantry system with the
aim of scanning 1 hectare (10$^4$) at around 1 mm$^2$ spatial resolution
multiple times per week. The system contains co-located sensors including a
stereo-pair RGB camera, a thermal imager, a laser scanner to capture 3D
structure, and two hyperspectral cameras covering wavelengths of 300-2500nm.
This sensor data is provided alongside over sixty types of traditional plant
phenotype measurements that can be used to train new machine learning models.
Associated weather and environmental measurements, information about agronomic
management and experimental design, and the genomic sequences of hundreds of
plant varieties have been collected and are available alongside the sensor and
plant phenotype data.
  Over the course of four years and ten growing seasons, the TERRA-REF system
generated over 1 PB of sensor data and almost 45 million files. The subset that
has been released to the public domain accounts for two seasons and about half
of the total data volume. This provides an unprecedented opportunity for
investigations far beyond the core biological scope of the project.
  The focus of this paper is to provide the Computer Vision and Machine
Learning communities an overview of the available data and some potential
applications of this one of a kind data.
