Abstract
With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively.
Highlights
Global nitrogen fertilizer use is expected to exceed 200 million tons in the year and continue to increase at 1.8% per year (FAO et al, 2015)
We present the proportion of variance explained by each model predictor, in terms of total variance explained by each predictor and the standard error of prediction (SEP)
The hybrids showed greater capacity for fertilizer uptake with higher grain yield (GY) when nitrogen is applied at sowing, as we reported above, which could have led to a higher tillering and crop canopy cover during vegetative growth as vegetation indices (VIs) indicated
Summary
Global nitrogen fertilizer use is expected to exceed 200 million tons in the year and continue to increase at 1.8% per year (FAO et al, 2015). The estimated increase in fertilizer use by 22.4% globally over the last 10 years (2004–2014) differs from an increase of 14.3% at the European level, where there is stronger and growing presence of precision agriculture management practices (Food and Agriculture Organization of the United Nations, 2017), but still demonstrates a growing demand for increased yields and an upwards trend in intensity of agricultural practices. This increase in fertilizer application only corresponded to barley (Hordeum vulgare) yield increases of 9.3 and 10.6% at the global and European scales, respectively. Though some previous studies have covered NUE and yield differences between two-row and six-row barley varieties (Le Gouis, 1992; Papastylianou, 1995; Le Gouis et al, 1999; Frégeau-Reid et al, 2001; del Moral et al, 2003; Arisnabarreta and Miralles, 2008), hybrid barley may represent an alternative in terms of higher yield and of improved growth and NUE (Gorny and Sodkiewicz, 2001; Kostadinova et al, 2016); to date there are no studies that we know of aimed at proving the effectiveness of remote sensing techniques as phenotyping tools for assessing the higher performance of hybrid barley in terms of growth, grain yield and NUE
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