A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding

TLDR

Remote sensing of plant canopies enables non‑intrusive, high‑throughput monitoring of physiological traits. The study compared UAV, proximal, and satellite remote sensing for measuring canopy temperature and NDVI to identify the most viable approach for large‑scale crop genetic improvement. UAVs collected high‑resolution thermal and multispectral imagery at 30–100 m altitude for hundreds of plots per flight, satellite imagery was sourced from 770 km altitude, and proximal data were obtained with IR thermometers and an NDVI sensor at 0.5–1 m, with all three platforms compared across elite cultivars and un‑adapted genetic resources under irrigated, drought, and varying thermal regimes. Airborne data correlated more strongly with yield and biomass at maturity than proximal measurements, supporting UAV-based remote sensing as a precise and efficient high‑throughput phenotyping tool.

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 0.5-1m 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 x 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.