Visible, Near Infrared, and Thermal Spectral Radiance On-Board UAVs for High-Throughput Phenotyping of Plant Breeding Trials

Abstract

Phenotyping in breeding trials is the basis for the selection of new varieties of food, feed and fibre crops that support the continued growth of world population. In addition to economic yield, breeders measure many phenotypes (also called traits) associated with the adaptation of these crops, such as the time of flowering, the height of the crop canopy, and the development of the canopy as it grows to maximum size and then senesces late in the season. The rapidly decreasing costs, and the convenience of use of UAV (unmanned aerial vehicles) is providing plant breeders with new tools with which to estimate some of the traits that are traditionally measured. With appropriate sets of measurements, it is possible to also estimate more complex traits, e.g. the radiation use efficiency (RUE) of a crop can be estimated when it is possible to track the change in light interception over time. Visual (RGB), thermal and multi-spectral cameras are key tools in monitoring crops by UAV, with LIDAR and hyperspectral instruments starting to come into use as they become sufficiently miniaturized. In this chapter we outline the use of these types of cameras in the characterization of plant phenotypes that assist breeders in the selection of genotypes, ideally at early stages of the breeding program. We outline the hierarchy of data values as they are transformed from raw data (L0) through calibrated normalized quantities (L1) to state variables (L2) and eventually to function traits (L3). Phenotypes that are directly observed by breeders are usually L2 traits, while L3 are derived traits, such as RUE, which are not directly measured by a sensor. We describe a workflow for managing and analysis UAV-captured imagery, and consider issues related to pixel resolution and camera parameters and the need for ‘local’ calibration approaches whereby a trait may be manually measured on a subset of plots, while being measured by UAV, in order to derive a predictive relationship from the subset to the entire trial. In the remaining part of the chapter, we provide examples and suggestions for improvements from our own research, based on the types of traits that are measured at the early, mid and late stages of the season as related to plant development, canopy cover and morphological traits, and segmentation of objects and spatial variation in signal intensity. Plant breeding needs to accelerate in order to keep up with population growth and changes in climate. Phenomics being developed with proximal sensing tools contributs to these accelerated breeding methods like genomic selection, and this chapter attempts to provide some guidelines for experts in remote sensing to engage in this area of research.