All Outputs (40)

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.

Rice plants overexpressing OsEPF1 show reduced stomatal density and increased root cortical aerenchyma formation (2019)
Journal Article
Mohammed, U., Caine, R. S., Atkinson, J. A., Harrison, E. L., Wells, D., Chater, C. C., Gray, J. E., Swarup, R., & Murchie, E. H. (2019). Rice plants overexpressing OsEPF1 show reduced stomatal density and increased root cortical aerenchyma formation. Scientific Reports, 9(1), Article 5584. https://doi.org/10.1038/s41598-019-41922-7

Stomata are adjustable pores in the aerial epidermis of plants. The role of stomata is usually described in terms of the trade-off between CO2 uptake and water loss. Little consideration has been given to their interaction with below-ground developme... Read More about Rice plants overexpressing OsEPF1 show reduced stomatal density and increased root cortical aerenchyma formation.

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.

Field phenotyping for the future (2018)
Journal Article
Atkinson, J. A., Jackson, R. J., Bentley, A. R., Ober, E., & Wells, D. M. (in press). Field phenotyping for the future. Annual Plant Reviews Online, 1(3), 719-736. https://doi.org/10.1002/9781119312994.apr0651

Global agricultural production has to double by 2050 to meet the demands of an increasing population and the challenges of a changing climate. Plant phenomics (the characterisation of the full set of phenotypes of a given species) has been proposed a... Read More about Field phenotyping for the future.

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.

An updated protocol for high throughput plant tissue sectioning (2017)
Journal Article
Atkinson, J. A., & Wells, D. M. (2017). An updated protocol for high throughput plant tissue sectioning. Frontiers in Plant Science, 8, https://doi.org/10.3389/fpls.2017.01721

Quantification of the tissue and cellular structure of plant material is essential for the study of a variety of plant sciences applications. Currently, many methods for sectioning plant material are either low throughput or involve free-hand section... Read More about An updated protocol for high throughput plant tissue sectioning.

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.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping (2017)
Journal Article
Pound, M. P., Atkinson, J. A., Townsend, A. J., Wilson, M. H., Griffiths, M., Jackson, A. S., Bulat, A., Tzimiropoulos, G., Wells, D. M., Murchie, E. H., Pridmore, T. P., & French, A. P. (2017). Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience, 6(10), Article gix083. https://doi.org/10.1093/gigascience/gix083

© The Author 2017. In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, h... Read More about Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

Linear discriminant analysis reveals differences in root architecture in wheat seedlings related to nitrogen uptake efficiency (2017)
Journal Article
Kenobi, K., Atkinson, J. A., Wells, D. M., Gaju, O., deSilva, J. G., Foulkes, M. J., Dryden, I. L., Wood, A. T., & Bennett, M. J. (2017). Linear discriminant analysis reveals differences in root architecture in wheat seedlings related to nitrogen uptake efficiency. Journal of Experimental Botany, 68(17), 4969-4981. https://doi.org/10.1093/jxb/erx300

© The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. Root architecture impacts water and nutrient uptake efficiency. Identifying exactly which root architectural properties influence these agronom... Read More about Linear discriminant analysis reveals differences in root architecture in wheat seedlings related to nitrogen uptake efficiency.

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.

Characterization of pearl millet root architecture and anatomy reveals three types of lateral roots (2016)
Journal Article
Passot, S., Gnacko, F., Moukouanga, D., Lucas, M., Guyomarc’h, S., Ortega, B. M., Atkinson, J. A., Belko, M. N., Bennett, M. J., Gantet, P., Wells, D. M., Guédon, Y., Vigouroux, Y., Verdeil, J.-L., Muller, B., & Laplaze, L. (2016). Characterization of pearl millet root architecture and anatomy reveals three types of lateral roots. Frontiers in Plant Science, 7(June2016), https://doi.org/10.3389/fpls.2016.00829

© 2016 Passot, Gnacko, Moukouanga, Lucas, Guyomarc’h, Moreno Ortega, Atkinson, Belko, Bennett, Gantet, Wells, Guédon, Vigouroux, Verdeil, Muller and Laplaze. Pearl millet plays an important role for food security in arid regions of Africa and India.... Read More about Characterization of pearl millet root architecture and anatomy reveals three types of lateral roots.

Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping (2016)
Preprint / Working Paper
Pound, M. P., Burgess, A. J., Wilson, M. H., Atkinson, J. A., Griffiths, M., Jackson, A. S., Bulat, A., Tzimiropoulos, G., Wells, D. M., Murchie, E. H., Pridmore, T. P., & French, A. P. Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-th... Read More about Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping.

Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat (2015)
Journal Article
Atkinson, J. A., Wingen, L. U., Griffiths, M., Pound, M. P., Gaju, O., Foulkes, M. J., Le Gouis, J., Griffiths, S., Bennett, M. J., King, J., & Wells, D. M. (2015). Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat. Journal of Experimental Botany, 66(8), 2283-2292. https://doi.org/10.1093/jxb/erv006

Seedling root traits of wheat (Triticum aestivum L.) have been shown to be important for efficient establishment and linked to mature plant traits such as height and yield. A root phenotyping pipeline, consisting of a germination paper-based screen c... Read More about Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat.

Branching out in roots: uncovering form, function, and regulation (2014)
Journal Article
Atkinson, J. A., Rasmussen, A., Traini, R., Voss, U., Sturrock, C., Mooney, S. J., Wells, D. M., & Bennett, M. J. (2014). Branching out in roots: uncovering form, function, and regulation. Plant Physiology, 166(2), 538-550. https://doi.org/10.1104/pp.114.245423

Root branching is critical for plants to secure anchorage and ensure the supply of water, minerals, and nutrients. To date, research on root branching has focused on lateral root development in young seedlings. However, many other programs of postemb... Read More about Branching out in roots: uncovering form, function, and regulation.

RootNav: navigating images of complex root architectures (2013)
Journal Article
Pound, M. P., French, A. P., Atkinson, J. A., Wells, D. M., Bennett, M. J., & Pridmore, T. (2013). RootNav: navigating images of complex root architectures. Plant Physiology, 162(4), 1802-1814. https://doi.org/10.1104/pp.113.221531

We present a novel image analysis tool that allows the semiautomated quantification of complex root system architectures in a range of plant species grown and imaged in a variety of ways. The automatic component of RootNav takes a top-down approach,... Read More about RootNav: navigating images of complex root architectures.