salinity stress

Taking the kinks out of curves

In a recent paper, researchers have developed a methodology suitable for analyzing the growth curves of a large number of plants from multiple families. The corrected curves accurately account for the spatial and temporal variations among plants that are inherent to high-throughput experiments.


An example of curve registration.  a The salinity sensitivity (SS) curves of the 16 functions from an arbitrary family, b SS curves after the curve registration, and c the corresponding time-warping functions. The salinity sensitivity on the y-axis of a and b refers to the derivative of the relative decrease in plant biomass


Advanced high-throughput technologies and equipment allow the collection of large and reliable data sets related to plant growth. These data sets allow us to explore salt tolerance in plants with sophisticated statistical tools.

As agricultural soils become more saline, analysis of salinity tolerance in plants is necessary for our understanding of plant growth and crop productivity under saline conditions. Generally, high salinity has a negative effect on plant growth, causing decreases in productivity.  The response of plants to soil salinity is dynamic, therefore requiring the analysis of growth over time to identify lines with beneficial traits.

In this paper the researchers, led by KAUST and including Dr Bettina Berger and Dr Chris Brien from the Australian Plant Phenomics Facility (APPF), use a functional data analysis approach to study the effects of salinity on growth patterns of barley grown at The Plant Accelerator® at the APPF. The method presented is suitable to reduce the noise in large-scale data sets and thereby increases the precision with which salinity tolerance can be measured.

Read the full paper, “Growth curve registration for evaluating salinity tolerance in barley” (DOI: 10.1186/s13007-017-0165-7) here.

Find out how the Australian Plant Phenomics Facility can support your plant science research here.


High-throughput phenotyping at The Plant Accelerator®



More salad please!

With indoor-vertical farming on the rise, lettuce production can be customised more than ever, by choosing the right varieties, temperature, lighting and nutrient supply to produce the leaves consumers want. Achieving this goal requires optimisation of numerous components and a recent collaborative study between the USA and Australia, published in Frontiers in Plant Science, has proven optical sensors can be used to evaluate lettuce growth, color and health non-destructively.

The research team, Ivan Simko and Ryan Hayes from the US Department of Agriculture and Robert Furbank from the ARC Centre of Excellence for Translational Photosynthesis and formerly Australian Plant Phenomics Facility – High Resolution Plant Phenomics Centre, designed the study to test the feasibility of using optical sensors for physiological evaluation of lettuce plants in early stages of their development. The method developed can help in breeding programs and optimising farming practices to meet the requirements of an increasingly demanding market.

Read the full study, Non-destructive phenotyping of lettuce plants in early stages of development with optical sensors, published in Frontiers in Plant Science, here.

Or read the abstract here:


Rapid development of plants is important for the production of ‘baby-leaf’ lettuce that is harvested when plants reach the four- to eight-leaf stage of growth. However, environmental factors, such as high or low temperature, or elevated concentrations of salt, inhibit lettuce growth. Therefore, non-destructive evaluations of plants can provide valuable information to breeders and growers. The objective of the present study was to test the feasibility of using non-destructive phenotyping with optical sensors for the evaluations of lettuce plants in early stages of development. We performed the series of experiments to determine if hyperspectral imaging and chlorophyll fluorescence imaging can determine phenotypic changes manifested on lettuce plants subjected to the extreme temperature and salinity stress treatments. Our results indicate that top view optical sensors alone can accurately determine plant size to approximately 7 g fresh weight.


Comparison of the size and the colour of plants cultivated at optimal (OPT), low (COLD) and high (HOT) temperatures (experiment 3). Plants were initially grown at OPT for 10 days and the either continuously kept in OPT or transferred to COLD or HOT for 8 days. Sides of the square pots are 68mm long.

Hyperspectral imaging analysis was able to detect changes in the total chlorophyll (RCC) and anthocyanin (RAC) content, while chlorophyll fluorescence imaging revealed photoinhibition and reduction of plant growth caused by the extreme growing temperatures (3 and 39°C) and salinity (100 mM NaCl). Though no significant correlation was found between Fv/Fm and decrease in plant growth due to stress when comparisons were made across multiple accessions, our results indicate that lettuce plants have a high adaptability to both low (3°C) and high (39°C) temperatures, with no permanent damage to photosynthetic apparatus and fast recovery of plants after moving them to the optimal (21°C) temperature. We have also detected a strong relationship between visual rating of the green- and red-leaf color intensity and RCC and RAC, respectively. Differences in RAC among accessions suggest that the selection for intense red color may be easier to perform at somewhat lower than the optimal temperature.


Genomic position of the quantitative trail locus (QTL) for light green colour (qLG4) on linkage group 4. Visual rating of the green colour intensity was performed on adult plants in field, while the relative chlorophyll content (RCC) was determined from hyperspectral reflectance measured on cotyledons of seedlings cultivated in plastic boxes (experiment 7). The orange line parallel with the linkage map shows the significance threshold (a = 0.05). The allele for light green colour and low RCC originates from cv. La Brilliante. Detailed description of the linkage map for this population and its construction was published previously (Hayes et al., 2014; Simko et al., 2015b). Distance in cM is shown on the right site of the linkage map. LOD, logarithm of odds.

This study serves as a proof of concept that optical sensors can be successfully used as tools for breeders when evaluating young lettuce plants. Moreover, we were able to identify the locus for light green leaf color (qLG4), and position this locus on the molecular linkage map of lettuce, which shows that these techniques have sufficient resolution to be used in a genetic context in lettuce.


Simko I, Hayes RJ and Furbank RT (2016) Non-destructive Phenotyping of Lettuce Plants in Early Stages of Development with Optical Sensors. Front. Plant Sci. 7:1985. doi: 10.3389/fpls.2016.01985