A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results

Abstract

A semi-automatic system was developed to monitor micro-plots of wheat cultivars in field conditions for phenotyping. The system is based on a hyperspectral radiometer and 2 RGB cameras observing the canopy from ~1.5 m distance to the top of the canopy. The system allows measurement from both nadir and oblique views inclined at 57.5° zenith angle perpendicularly to the row direction. The system is fixed to a horizontal beam supported by a tractor that moves along the micro-plots. About 100 micro-plots per hour were sampled by the system, the data being automatically collected and registered thanks to a centimetre precision geo-location. The green fraction (GF, the fraction of green area per unit ground area as seen from a given direction) was derived from the images with an automatic segmentation process and the reflectance spectra recorded by the radiometers were transformed into vegetation indices (VI) such as MCARI2 and MTCI. Results showed that MCARI2 is a good proxy of the GF, the MTCI as observed from 57° inclination is expected to be mainly sensitive to leaf chlorophyll pigments. The frequent measurements achieved allowed a good description of the dynamics of each micro-plot along the growth cycle. It is characterised by two periods: the first period corresponding to the vegetative stages exhibits a small rate of change of VI with time; followed by the senescence period characterised by a high rate of change. The dynamics were simply described by a bilinear model with its parameters providing high throughput metrics (HTM). A variance analysis achieved over these HTMs showed that several HTMs were highly heritable, particularly those corresponding to MCARI2 as observed from nadir, and those corresponding to the first period. Potentials of such semi-automatic measurement system are discussed for in field phenotyping applications.

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

“…The high spectral resolution provided by current sensors allows computing narrow-band VIs that can detect subtle changes in reflectance, e.g., the red-edge position that relates to both the LAI and leaf chlorophyll content (Cho and Skidmore 2006;Clevers and Kooistra 2012;Guyot and Baret 1988). These VIs can be used to estimate either structural properties such as LAI (Darvishzadeh et al, 2011;Haboudane et al, 2004) and green fraction (Comar et al, 2012), or biochemical properties such as leaf chlorophyll content ) and leaf water content (Colombo et al, 2008). However, interactions between biochemical and structural canopy parameters (e.g., LAI and leaf chlorophyll content) may add some uncertainties in the retrieval, the measured signal not only depending on the leaf biochemistry but also on the amount of leaves within the sensor field of view (Baret et al 2007;Colombo et al 2008).…”

“…/j.fcr.2017 green fraction, leaf area index (LAI), and the leaf biochemistry, e.g., chlorophyll and nitrogen contents (Comar et al, 2012;Jay et al, 2015;Thorp et al, 2015). An accurate retrieval of such structural and biochemical parameters is critical for plant phenotyping.…”

“…However, cultivar selection requires a deep understanding of how the plant genetic makeup (genotype) relates to the observable plant traits (phenotypes), and how the genes express themselves in a given environment (Furbank and Tester, 2011). Significant advances in genomics and gene technology have been done in the past decades, thus making in-field highthroughput phenotyping one of the major bottlenecks in plant breeding (Comar et al, 2012;Furbank and Tester, 2011;Montes et al, 2007). As phenotypes must be characterized over time, nondestructive techniques have to be developed.…”

Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum

“…Tractor-mounted systems are manned vehicles, although some are modified to be autonomous, on which mounted sensors offer close spatial proximity to individual plants. Tractors are normally available at agricultural field sites and capable of carrying large payloads; however, tractors have limitations when phenotyping is considered including: large size, internal combustion engines, vertical clearance [normally 1.4-2.0 m (Comar et al 2012;Busemeyer et al 2013)], limited mobility, inability to operate after irrigation or precipitation and the necessity of experienced operators (White et al 2012). Despite these limitations, tractor systems have been used for field-based phenotyping.…”

“…Busemeyer et al (2013) developed a tractor-pulled phenotyping platform for small grain cereals, equipped with a wide range of optical sensors. Comar et al (2012) built a semi-autonomous tractor system equipped with four spectrometers, a hyperspectral radiometer and two RGB cameras which covered 100 microplots per hour. Both of these platforms used internal combustion engines (Andrade-Sanchez et al 2014;Comar et al 2012) which have the potential to be disruptive.…”