title: A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding.
abstract: Remote sensing (RS) of plant canopies permits non-intrusive, high-throughput monitoring of plant physiological characteristics. This study compared three RS approaches using a low flying UAV (unmanned aerial vehicle), with that of proximal sensing, and satellite-based imagery. Two physiological traits were considered, canopy temperature (CT) and a vegetation index (NDVI), to determine the most viable approaches for large scale crop genetic improvement. The UAV-based platform achieves plot-level resolution while measuring several hundred plots in one mission via high-resolution thermal and multispectral imagery measured at altitudes of 30-100 m. The satellite measures multispectral imagery from an altitude of 770 km. Information was compared with proximal measurements using IR thermometers and an NDVI sensor at a distance of 0.5-1 m above plots. For robust comparisons, CT and NDVI were assessed on panels of elite cultivars under irrigated and drought conditions, in different thermal regimes, and on un-adapted genetic resources under water deficit. Correlations between airborne data and yield/biomass at maturity were generally higher than equivalent proximal correlations. NDVI was derived from high-resolution satellite imagery for only larger sized plots (8.5 × 2.4 m) due to restricted pixel density. Results support use of UAV-based RS techniques for high-throughput phenotyping for both precision and efficiency.
authors.affiliation: International Maize and Wheat Improvement Center Texcoco, Mexico.
authors.forename: Maria
authors.initials: M
authors.lastname: Tattaris
authors.affiliation: International Maize and Wheat Improvement Center Texcoco, Mexico.
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Reynolds
authors.affiliation: CSIRO Agriculture, Queensland Bioscience Precinct Queensland, QLD, Australia.
authors.forename: Scott C
authors.initials: SC
authors.lastname: Chapman
keywords: UAV; airborne imagery; high-throughput phenotyping; indices; multispectral; thermal