Outputs (5)

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures (2019)
Journal Article
Yasrab, R., Atkinson, J. A., Wells, D. M., French, A. P., Pridmore, T. P., & Pound, M. P. (2019). RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures. GigaScience, 8(11), Article giz123. https://doi.org/10.1093/gigascience/giz123

BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentat... Read More about RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

Soil strength influences wheat root interactions with soil macropores (2019)
Journal Article
Atkinson, J. A., Hawkesford, M. J., Whalley, W. R., Zhou, H., & Mooney, S. J. (2020). Soil strength influences wheat root interactions with soil macropores. Plant, Cell and Environment, 43(1), 235-245. https://doi.org/10.1111/pce.13659

Deep rooting is critical for access to water and nutrients found in subsoil. However, damage to soil structure and the natural increase in soil strength with depth, often impedes root penetration. Evidence suggests that roots use macropores (soil cav... Read More about Soil strength influences wheat root interactions with soil macropores.

Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures" (2019)
Data
French, A., Wells, D. M., Atkinson, J., Pound, M., Yasrab, R., & Pridmore, T. (2019). Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures". [Data]. https://doi.org/10.5524/100651

We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously... Read More about Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures".

High throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in leaves under controlled gaseous conditions (2019)
Journal Article
McAusland, L., Murchie, E., & Atkinson, J. (2019). High throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in leaves under controlled gaseous conditions. Plant Methods, 15, Article 109. https://doi.org/10.1186/s13007-019-0485-x

© 2019 The Author(s). Background: As yields of major crops such as wheat (T. aestivum) have begun to plateau in recent years, there is growing pressure to efficiently phenotype large populations for traits associated with genetic advancement in yield... Read More about High throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in leaves under controlled gaseous conditions.

Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat (2019)
Other
Griffiths, M., Atkinson, J. A., Gardiner, L.-J., Swarup, R., Pound, M. P., Wilson, M. H., …Wells, D. M. (2019). Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat

The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource dependent response in wheat (Triticum aestivum L.... Read More about Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat.