Abstract
A plant phenotyping approach was applied to evaluate growth rate of containerized tree seedlings during the precultivation phase following seed germination. A simple and affordable stereo optical system was used to collect stereoscopic red–green–blue (RGB) images of seedlings at regular intervals of time. Comparative analysis of these images by means of a newly developed software enabled us to calculate (a) the increments of seedlings height and (b) the percentage greenness of seedling leaves. Comparison of these parameters with destructive biomass measurements showed that the height traits can be used to estimate seedling growth for needle-leaved plant species whereas the greenness trait can be used for broad-leaved plant species. Despite the need to adjust for plant type, growth stage and light conditions this new, cheap, rapid, and sustainable phenotyping approach can be used to study large-scale phenome variations due to genome variability and interaction with environmental factors.
Highlights
Worldwide, an estimated two billion ha of forests are degraded (Minnemayer et al, 2011; Stanturf et al, 2014)
The small size characteristic makes the system transportable and combinable with other equipment as well as with high potential to be straightforward integrated in mass-industry. We describe this in-house developed optical system together with the results obtained from a growth kinetics study on tree seedlings grown in a growth chamber, from seed germination to 5-weeks-old plants
Seeds of F. sylvatica were first hydrated by soaking for 24 h in tap water; seeds were surface sterilized with 3,5% household bleach for 2 min, and rinsed four times with sterile water to remove all traces of bleach
Summary
An estimated two billion ha of forests are degraded (Minnemayer et al, 2011; Stanturf et al, 2014). Measurements of relative growth rate on a mass basis still depend on destructive and time-consuming approaches (Walter et al, 2007; Fiorani and Schurr, 2013; Humplík et al, 2015) with the result of limiting the possibility to examine (1) a large number of samples enabling metadata analysis, and (2) the same sample repeatedly over time (Furbank and Tester, 2011; Busemeyer et al, 2013; Rahaman et al, 2015) To overcome these constraints and to increase the usefulness of phenotype investigation, new approaches based upon the use of technologically advanced equipment that do not affect the samples under examination have been attempted (Tsaftaris and Noutsos, 2009; Walter et al, 2015). We present a comparison between the data obtained by automated imaging analysis with those obtained with the traditional destructive method
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