Relevant Literature:

Review: An integrated framework for crop adaptation to dry environments: Responses to transient and terminal drought. Berger et al. (2016) Plant Science 253.

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols. Bodner et al. (2017) Journal of Visualized Experiments, 126.

Characterising root trait variability in chickpea (Cicer arietinum L.) germplasm. Chen et al. (2017) Journal of Experimental Botany, 68:8.

Root Gap Correction with a Deep Inpainting Model. Chen et al. (2018)

High-throughput two-dimensional root system phenotyping platform facilitates genetic analysis of root growth and development. Clark et al. (2013) Plant Cell & Environment, 36:2.

Rhizobial strain involvement in symbiosis efficiency of chickpea–rhizobia under drought stress: plant growth, nitrogen fixation and antioxidant enzyme activities. Esfahani et al. (2011) Acta Physiologiae Plantarum, 33:4.

Imaging and Analysis Platform for Automatic Phenotyping and Trait Ranking of Plant Root Systems. Iyer-Pascuzzi et al. (2010) Plant Physiology, 152:3.

Wheat root growth responses to horizontal stratification of fertiliser in a water-limited environment. Jin et al. (2015) Plant Soil, 386:1-2.

Advancements in Root Growth Measurement Technologies and Observation Capabilities for Container-Grown Plants. Judd et al. (2015) Plants, 4:3.

Scope for improvement of yield under drought through the root traits in chickpea (Cicer arietinum L.). Kashiwagi et al. (2015) Field Crops Research, 170.

Root phenotyping: from component trait in the lab to breeding. Kuijken et al. (2015) Journal of Experimental Botany, 66:18.

An evaluation of inexpensive methods for root image acquisition when using rhizotrons. Mohamed et al. (2017) Plant Methods, 13:11.

GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. Nagel et al. (2012) Functional Plant Biology, 39:11.

Response of chickpea (Cicer arietinum L.) to terminal drought: leaf stomatal conductance, pod abscisic acid concentration, and seed set. Pang et al. (2017) Journal of Experimental Botany, 68:8.

Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping. Pound et al. (2016) GigaScience, 6:10.

Shoot traits and their relevance in terminal drought tolerance of chickpea (Cicer arietinum L.). Purushothaman et al. (2016) Field Crops Research, 197.

Root traits confer grain yield advantages under terminal drought inchickpea (Cicer arietinum L.). Purushothaman et al. (2017) Field Crops Research, 201.

GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown root systems. Rellán-Álvarez et al. (2015) eLife.

Machine Learning for High-Throughput Stress Phenotyping in Plants. Singh et al. (2016) Trends in Plant Science, 21:2.

A simulation study of chickpea crop response to limited irrigation in a semiarid environment. Soltani et al. (2001) Agricultural Water Management, 49:3.

RhizoChamber-Monitor: a robotic platform and software enabling characterization of root growth. Wu et al. (2018) Plant Methods, 14:44.

A conservative pattern of water use, rather than deep or profuse rooting, is critical for the terminal drought tolerance of chickpea. Zaman-Allah et al. (2011) Journal of Experimental Botany, 62:12.

Rhizobium-Legume Symbiosis and Nitrogen Fixation under Severe Conditions and in an Arid Climate. Zahran et al. (1999) Microbiolgy and Molecular Biology Reviews, 63:4.

Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root Traits. Zhao et al. (2016) Frontiers in Plant Science, 7.