RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Abstract

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants

“…Soil-filled systems with one transparent side for root system visualization have previously been developed (e.g. Sachs 1865, Price et al ., 2002, Devienne-Barret et al ., 2006, Bodner et al ., 2017). These systems differ in their dimensions, and hence, the volume of soil available for root system growth.…”

“…Other soil-based growth systems provide relatively thin layers of soil bordered by one or two transparent surfaces to visualise roots pressed against them, thus collapsing a variable fraction of the entire root system in 3D against a transparent surface for a 2D representation (Neumann et al , 2009). Such 2D systems have been reported for the dicotyledonous species Arabidopsis thaliana (Devienne-Barret et al , 2006, Rellan-Alvarez et al , 2015), tomato (Dresbøll et al , 2013, Rellan-Alvarez et al , 2015), lupine (Leitner et al ., 2014), sugar beet (Bodner et al , 2017), or monocots such as rice (Price et al , 2002, Shrestha et al , 2014), and wheat (Jin et al , 2015). These systems have allowed testing of plant growth behaviour in waterlogging (Dresbøll et al , 2013), low moisture stress (Avramova et al , 2016, Durand et al , 2016) or contrasting nutrient availability conditions (Jin et al , 2015).…”

Crop Biotechnology for Improving Drought Tolerance: Targets, Approaches, and Outcomes

“…However, given the root's essential functions across developmental stages, the researcher's attention to this plant organ has substantially increased in the last decade. This new-found interest in below-ground traits has also been spurred on by modern phenotyping techniques including fluorescent reporters, low-throughput high-resolution 3D methods such as CT and MRI, as well higher-throughput methods such as RGB, hyperspectral, and ultrawide-band imaging (Bodner et al, 2017;Truong et al, 2018). The knowledge obtained through these analyses and the establishment of other novel experimental methods to explore crop root biology and its responses in solum have opened a broader range of possibilities for yield improvement through the optimisation of root systems.…”

Raspberry Pi Powered Imaging for Plant Phenotyping

“…Here, we document three different methods for using Raspberry Pi computers for plant phenotyping (Figure 1.). These protocols are a valuable resource because while there are many phenotyping papers that outline phenotyping systems in detail (Granier et al, 2006; Iyer-Pascuzzi et al, 2010; Jahnke et al, 2016; Shafiekhani et al, 2017), there are few protocols that provide step-by-step instructions for building them (Bodner et al, 2017; Minervini et al, 2017). We provide examples illustrating automation of photo capture with open source tools (based on Python and standard Linux utilities).…”