7. PLANT PHENOMICS TO SCREEN TRAITS FOR ADAPTING TO STRESSES
7.2. High-Throughput Imaging to Diagnose and Quantify Plant Response
Phenomics has also advanced to develop other high-throughput phenomics technologies, a few of which include plant response to biotic and abiotic stresses (Matouš et al., 2006; Chaerle et al., 2007; Jones et al., 2009; Sirault et al., 2009; Berger et al., 2010; Munns et al., 2010; Furbank and Tester, 2011), dissecting dynamic changes in plant structure and functions ( Jahnke et al., 2009), multisensor stress catalog (Chaerle et al., 2009), or module for handling large-scale phenotyping and genotyping data ( Jung et al., 2011), which is beyond the scope of this chapter to provide procedural and techni- cal details (Lenk et al., 2007). Nonetheless, we highlight their applications in agriculture including in plant breeding to developing cultivars with better adaptation to stress-prone environments.
Thermal and chlorophyll fluorescence imaging are powerful tools for the study of spatial and temporal heterogeneity of leaf transpiration and photosynthetic performance. Chlorophyll fluorescence imaging provides a powerful, noninvasive tool for investigating photosynthesis and its het- erogeneity, and the variations in fluorescence transients could be used for early detection of biotic and abiotic stresses. Matouš et al. (2006) developed a new technique of combinatorial fluorescence imaging that significantly enhances our capacity to reveal dynamics of the plant–pathogen interac- tion. These images yield the highest contrast throughout the progres- sion of infection in plants, which can be divided into segments that show tissue in different infection phases, as Matouš et al. demonstrated in case of
fluorescence imaging, when combined, provide specific signatures for diag- nosis of distinct diseases and abiotic stresses, for example, rapid screening for stomatal responses can be achieved by thermal imaging, while, com- bined with fluorescence imaging to study photosynthesis, it can poten- tially be used to derive leaf WUE as a screening parameter (Chaerle et al., 2007). These images allow continuous automated monitoring of dynamic spatial variation. Moreover, such dual-imaging systems could be extended with complementary techniques such as hyperspectral and blue-green fluorescence imaging, which will increase the power of stress diagnosis and the potential for screening of stress-tolerant germplasm (Chaerle et al., 2007). Likewise, using hyperspectral and chlorophyll fluorescence imaging,
Bauriegel et al. (2011) were able to detect wheat plots infected with head blight (F. culmorum). The disease severity was highly correlated with photo- synthetic efficiency and above the infection limit of 5% severity of disease, chlorophyll fluorescence imaging reliably detected infected ears.
Salinity is another major abiotic stress affecting crops production world- wide, which can be exacerbated by the changing climate. Sirault et al. (2009)
developed a high-throughput automated image protocol that captures, identifies and analyzes thermal images acquired from a long-wave infrared (IR) camera to quantify the osmotic stress response of wheat and barley to salts. This technology in comparison to porometry ( James et al., 2008) measures stomatal conductance through a precise, noninvasive and quick method, and needs only few seconds to acquire a thermograph comprising more than 3000 individual measurements for each object. It also takes into account spatial heterogeneity. IR thermal imaging could be therefore used to screen a large numbers of germplasm accessions, breeding lines and cul- tivars varying in the stomatal traits related to salt tolerance.
Drought is a complex stress that elicits a wide variety of plant responses. The nondestructive imaging techniques allow a temporal resolution and monitoring of the same plants throughout the experiment, which provides vital information on the physiological changes in response to drought over time to identify and characterize drought-response mechanisms into a series of component traits (Berger et al., 2010). A number of nondestructive imag- ing methods are now available to study physiological changes occurring in plants under drought, which include thermal IR imaging or IR thermog- raphy to assess transpiration rates (Trs), NIR to assess growth and water stress of plants, or fluorescence imaging to complement reflectance imag- ing. However, researchers should be aware of technological challenges that should be properly addressed to avoid erroneous results. It is thus clear that
trait-dissection affected by high-throughput phenotyping could provide a significant new opportunity to enhancing our knowledge of plant response to drought, including elucidating the genetic basis of these responses and integrating such traits into appropriate combinations to improve crops per- formance under varying drought conditions (Berger et al., 2010; references therein; Furbank and Tester, 2011).
Agricultural production is limited by various abiotic and biotic stresses. Imaging technology has advanced to provide valuable information on plant stresses; and when supplemented by conventional video imagery (for study of growth), they provide an efficient early warning system to discriminate between different stressors to develop matrix that identifies specific signa- tures for multiple stress types, which allow agriculturists to reduce produc- tion losses in stress-prone environments (Chaerle et al., 2009).
Maize researchers took the lead to adapt some of the above high- throughput phenotyping tools to study variation in maize hybrids in response to drought under field conditions. Using high-throughput sensors,
Winterhalter et al. (2011) demonstrated that canopy water mass (CWM, amount of water kg m−2) correlates well (r2 > 0.70) with spectral indi-
ces and IR temperature with varying drought-stress levels, which enabled them to differentiate hybrids into three groups (above, below or average performer) under control and stressed environments. Their finding dem- onstrates that it is indeed possible to both detect CWM and discriminate between groups of maize hybrids using nondestructive high-throughput phenotyping, thus, potentially a useful technique for crop breeding. Resil- ience to soil water deficit and its genetics are a priority research area for maize breeding. More recently, Chapuis et al. (2012) tested and compared three methods of determining resilience to water deficit: the ability of hybrids to maintain leaf growth in a range of soil water potentials in a phe- notyping platform, a direct estimator of resilience of seed number to water deficit in a network of field experiments, and classical methods involv- ing adaptation to drought indices and variance analysis. They evaluated 19 maize hybrids by growing in 14 environments. Using the slope of regres- sion line between drought index and seed number (which was taken as an estimate of the resilience to soil water deficit for each hybrid), Chapuis et al. (2012) detected twofold differences that correlated with resilience of leaf growth to soil water deficit in the phenotyping platform. Resilience estimated via genotype × watering treatment was nonsignificant due to large differences in drought indices between genotypes in a given water- ing treatment. This result led them to propose that the direct estimation of
resilience to water deficit is feasible in the field with a minimum amount of environmental measurements.
O’Shaughnessy et al. (2011) used canopy temperature data to investi- gate whether an empirical crop water stress index (CWSIe) could be used to monitor spatial and temporal crop water stress in soybean and cotton. Using the benchmark relationships between CWSIe and leaf water poten- tial (ΨL), they detected significant negative linear correlation between mid- day ΨL measurements and CWSIe at well-established soil water differences. Average seasonal CWSIe values were inversely related to crop water use (r 2 values > 0.89 for soybean and 0.55 for cotton). O’Shaughnessy et al. (2011) also detected a significant inverse relationship between the CWSIe and soybean (2-year average r2 = 0.85) and cotton (r2 = 0.78 in 1 year) yield,
which enabled them to conclude that contour plots of CWSIe may be used as maps to indicate the spatial variability of within-field crop water stress; and thus may be useful for scheduling irrigation or identifying areas within a field where water stress may impact crop water use and yield.