All Outputs (6)

Deep learning for multi-task plant phenotyping (2017)
Conference Proceeding
Pound, M. P., Atkinson, J. A., Wells, D. M., Pridmore, T. P., & French, A. P. (2017). Deep learning for multi-task plant phenotyping. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017 (2055-2063). https://doi.org/10.1109/ICCVW.2017.241

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.

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., …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.

Plant phenomics, from sensors to knowledge (2017)
Journal Article
Tardieu, F., Cabrera-Bosquet, L., Pridmore, T. P., & Bennett, M. J. (2017). Plant phenomics, from sensors to knowledge. Current Biology, 27(15), R770-R783. https://doi.org/10.1016/j.cub.2017.05.055

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants)... Read More about Plant phenomics, from sensors to knowledge.

Quantification of root water uptake in soil using X-ray computed tomography and image-based modelling: Quantification of root water uptake in soil (2017)
Journal Article
Daly, K. R., Tracy, S. R., Crout, N. M., Mairhofer, S., Pridmore, T. P., Mooney, S. J., & Roose, T. (2018). Quantification of root water uptake in soil using X-ray computed tomography and image-based modelling: Quantification of root water uptake in soil. Plant, Cell and Environment, 41(1), 121-133. https://doi.org/10.1111/pce.12983

Spatially averaged models of root-soil interactions are often used to calculate plant water uptake. Using a combination of X-ray Computed Tomography (CT) and image based modelling we tested the accuracy of this spatial averaging by directly calculati... Read More about Quantification of root water uptake in soil using X-ray computed tomography and image-based modelling: Quantification of root water uptake in soil.

Root hydrotropism is controlled via a cortex-specific growth mechanism (2017)
Journal Article
Dietrich, D., Pang, L., Kobayashi, A., Fozard, J. A., Boudolf, V., Bhosale, R., …Bennett, M. J. (2017). Root hydrotropism is controlled via a cortex-specific growth mechanism. Nature Plants, 3(6), Article 17057. https://doi.org/10.1038/nplants.2017.57

Plants can acclimate by using tropisms to link the direction of growth to environmental conditions. Hydrotropism allows roots to forage for water, a process known to depend on abscisic acid (ABA) but whose molecular and cellular basis remains unclear... Read More about Root hydrotropism is controlled via a cortex-specific growth mechanism.

SuRVoS: Super-Region Volume Segmentation workbench (2017)
Journal Article
Luengo, I., Darrow, M. C., Spink, M. C., Sun, Y., Dai, W., He, C. Y., …French, A. P. (2017). SuRVoS: Super-Region Volume Segmentation workbench. Journal of Structural Biology, 198(1), 43-53. https://doi.org/10.1016/j.jsb.2017.02.007

Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic seg... Read More about SuRVoS: Super-Region Volume Segmentation workbench.