Infra-red Thermography for High Throughput Field Phenotyping in Solanum tuberosum

Abstract

The rapid development of genomic technology has made high throughput genotyping widely accessible but the associated high throughput phenotyping is now the major limiting factor in genetic analysis of traits. This paper evaluates the use of thermal imaging for the high throughput field phenotyping of Solanum tuberosum for differences in stomatal behaviour. A large multi-replicated trial of a potato mapping population was used to investigate the consistency in genotypic rankings across different trials and across measurements made at different times of day and on different days. The results confirmed a high degree of consistency between the genotypic rankings based on relative canopy temperature on different occasions. Genotype discrimination was enhanced both through normalising data by expressing genotype temperatures as differences from image means and through the enhanced replication obtained by using overlapping images. A Monte Carlo simulation approach was used to confirm the magnitude of genotypic differences that it is possible to discriminate. The results showed a clear negative association between canopy temperature and final tuber yield for this population, when grown under ample moisture supply. We have therefore established infrared thermography as an easy, rapid and non-destructive screening method for evaluating large population trials for genetic analysis. We also envisage this approach as having great potential for evaluating plant response to stress under field conditions.

Infra-Red Thermography as a High-Throughput Tool for Field Phenotyping

“…It has been confirmed that different cameras can produce similar screening results, thus enhancing the potential capacity of phenotyping systems; for example in one case on vineyards temperatures recorded by two cameras (SnapShot 525, a 120 × 120 pixel line scan imager in the 8-12 μm wavebands and ThermaCAM SC2000, a long-wave imager with a 320 × 240 pixel sensor) were highly correlated (0.94) [35]. Other experiments have confirmed that the thermal ranking of genotypes can be maintained over measurements made on different days, times of day and at different sites and temperature-related quantitative trait loci (QTLs) identified [25,29,60,61]. For high-throughput phenotyping it is not usually necessary to estimate stomatal conductance accurately (e.g., using reference surfaces or models) and relative measures are usually adequate though it remains critical to ensure that data are not biased by background noise due to soil or woody parts.…”

“…Furthermore the temperature at any time is not only dependent on the current equilibrium value but also to some extent on previous conditions and the thermal lags in the system [2,27,28]. A particularly useful approach to the partial elimination in phenotyping studies of the rapid or slow changes in equilibrium temperature caused by environmental variation has been described by Jones and colleagues [25,29]; this involves simultaneous collection of temperatures from a number of plots in each image and expressing individual plot temperatures as differences from the mean of all plots in that image. This corrects for temporal changes in temperature as occur, for example, when clouds pass over.…”

“…The power of this correction method improves as the number of plots in an image increases and as the amount of overlap between images increases, and works well even if the environmental conditions are changing rapidly. Figure 1 illustrates the range of normalised genotype variation for a potato trial imaged on three different days (see [29] for details) showing the temperature variation on each day caused by variation in factors such as air temperature, relative humidity and solar radiation. Box plot demonstrating the range of temperature variation for trials imaged on different days (see [29] for details).…”

“…Figure 1 illustrates the range of normalised genotype variation for a potato trial imaged on three different days (see [29] for details) showing the temperature variation on each day caused by variation in factors such as air temperature, relative humidity and solar radiation. Box plot demonstrating the range of temperature variation for trials imaged on different days (see [29] for details). Trial4_Day1 displays a greater variation in temperature during imaging as compared with Trial4_Day 2 and Trial4_Day 3 as a result of greater variation in solar radiation.…”

“…Wide angle lenses lead to greater variation in the BRDF across an image resulting, for example, in a wide range of the fraction of shaded pixels for such images and a lack of comparability of observed temperatures across an image. Although the BRDF can substantially impact measurements where the view-illumination angle varies substantially between different plots in any image for a phenotyping experiment, such problems can be minimised by taking multiple overlapping images and deriving a genotype mean from many individual replicate plots with differing view angles [29].…”