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.

Keywords: UAV; airborne imagery; high-throughput phenotyping; indices; multispectral; thermal.

Figure 1

The airborne remote sensing platform used in this study: The AscTec Falcon 8 Unmanned Aerial Vehicle (UAV), operated with the Mobile Ground Station (inset).

Figure 2

(A). Raw image of Gen Res DRT trial within the drought environment, taken using the ADC Lite Tetracam on the UAV, approximately at 100 m height. Ground dimensions of plots are 2 × 0.8 m, with arrows representing direction of proximal measurements. Assuming a measurement time of 10 s per plot, the time taken to complete measurements using proximal sensors is ~69 min for this trial, compared to several seconds with the UAV. (B) Raw image of a “HOT” trial extracted from video footage from the FLIR Tau thermal camera. Flight altitude was ~30 m. Ground dimensions of plots are 2 × 0.8 m. (C) Pan-sharpened WV-2 imagery of Elite OPT. Pan-sharpened imagery of a trial containing smaller sized plots in (D) did not allow for the extraction of NDVI as plots were mixed within pixels.

Figure 3

Example of the image processing using UAV-mounted FLIR Tau image of “HOT” trial shown in Figure 2B where a mask is applied to remove any non-vegetation pixels by applying a threshold for each pixel value. This is followed by the detection of each plot using pre-defined location parameters (red rectangles) and the removal of high variance pixels (using histogram of the pixel values of each plot). An average of pixel values over each band is taken to get a value per band per plot. This value is then subsequently used to calculate the target indices.