Outputs (32)

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., Bennett, M. J., & 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.

Demystifying roots: a need for clarification and extended concepts in root phenotyping (2018)
Journal Article
Lobet, G., Paez-Garcia, A., Schneider, H., Junker, A., Atkinson, J. A., & Tracy, S. (2018). Demystifying roots: a need for clarification and extended concepts in root phenotyping. Plant Science, 282, 11-13. https://doi.org/10.1016/j.plantsci.2018.09.015

Plant roots have major roles in plant anchorage, resource acquisition and offer environmental benefits including carbon sequestration and soil erosion mitigation. As such, the study of root system architecture, anatomy and functional properties is of... Read More about Demystifying roots: a need for clarification and extended concepts in root phenotyping.

Erratum to: Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies (2018)
Journal Article
Atkinson, J. A., Lobet, G., Noll, M., Meyer, P. E., Griffiths, M., & Wells, D. M. (2018). Erratum to: Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies. GigaScience, 7(7), Article giy043. https://doi.org/10.1093/gigascience/giy043

In the final publication of "Combining semi-automated image analysis techniqueswith machine learning algorithms to accelerate large-scale genetic studies," by Jonathan Atkinson A. et al.,[1] the first column heading for Table 1 was incorrect.

Fur... Read More about Erratum to: Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

Uncovering the hidden half of plants using new advances in root phenotyping (2018)
Journal Article
Atkinson, J. A., Pound, M. P., Bennett, M. J., & Wells, D. M. (2019). Uncovering the hidden half of plants using new advances in root phenotyping. Current Opinion in Biotechnology, 55, 1-8. https://doi.org/10.1016/j.copbio.2018.06.002

© 2018 The Authors 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 r... Read More about Uncovering the hidden half of plants using new advances in root phenotyping.

Deep Learning for Multi-task Plant Phenotyping (2017)
Preprint / Working Paper
Pound, M. P., Atkinson, J. A., Wells, D. M., Pridmore, T. P., & French, A. P. (2017). Deep Learning for Multi-task Plant Phenotyping

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recent... Read More about Deep Learning for Multi-task Plant Phenotyping.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies (2017)
Journal Article
Atkinson, J. A., Lobet, G., Noll, M., Meyer, P. E., Griffiths, M., & Wells, D. M. (2017). Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies. GigaScience, 6(10), https://doi.org/10.1093/gigascience/gix084

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a... Read More about Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies (2017)
Other
Atkinson, J. A., Lobet, G., Noll, M., Meyer, P. E., Griffiths, M., & Wells, D. M. (2017). Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

Background: Genetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to e... Read More about Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies.