A core objective of the TERRA-REF project was to generate an open-access reference dataset for the study of evaluation of sensing technology 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 (~$10^4$ m) 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 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 trait (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. This 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.

TERRA-REF项目产生了一项开放式的参考数据集，其中包括高分辨率的传感器数据和超过60种传统的植物表型测量，可以用于训练新的机器学习模型。该研究旨在向计算机视觉和机器学习界提供有关该数据及其潜在应用的概述。

TERRA-REF的高分辨率、多传感器植物感知公共领域数据对计算机视觉社区有何贡献？