Uncovering the hidden half of plants using new advances in root phenotyping

• Published in: Current opinion in biotechnology Vol. 55; pp. 1 - 8
• Main Authors: Atkinson, Jonathan A, Pound, Michael P, Bennett, Malcolm J, Wells, Darren M
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2019; Elsevier
• Subjects: automation; climate change; crop yield; crops; food security; image analysis; Imaging, Three-Dimensional; nutrient use efficiency; Phenotype; Plant Roots - anatomy & histology; population growth; Software; Tomography
• ISSN: 0958-1669; 1879-0429; 1879-0429
• DOI: 10.1016/j.copbio.2018.06.002

[Display omitted] •A ‘second green revolution’ using crops with improved below-ground traits is needed.•Phenotyping plant roots poses practical and data challenges.•Technologies for 2D root phenotyping include rhizotrons, paper pouches, and plates.•MRI, PET and X-ray CT allow 3D and 4D root phenotyping in soil.•Non-invasive techniques for field phenotyping have recently advanced.•Deep machine learning techniques are transforming the root phenotyping landscape. Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e. its structure and function) represents a major bottleneck. We describe how advances in imaging and sensor technologies are making root phenomic studies possible. However, methodological advances in acquisition, handling and processing of the resulting ‘big-data’ is becoming increasingly important. Advances in automated image analysis approaches such as Deep Learning promise to transform the root phenotyping landscape. Collectively, these innovations are helping drive the selection of the next-generation of crops to deliver real world impact for ongoing global food security efforts.