All Outputs (4)

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), https://doi.org/10.1093/gigascience/giz123

© The Author(s) 2019. Published by Oxford University Press. 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... Read More about RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

Recovering Wind-induced Plant motion in Dense Field Environments via Deep Learning and Multiple Object Tracking (2019)
Journal Article
Gibbs, J. A., Burgess, A. J., Pound, M. P., Pridmore, T. P., & Murchie, E. H. (2019). Recovering Wind-induced Plant motion in Dense Field Environments via Deep Learning and Multiple Object Tracking. Plant Physiology, 181, 28-42. https://doi.org/10.1104/pp.19.00141

Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m... Read More about Recovering Wind-induced Plant motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram (2019)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2019). A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram. Journal of Synchrotron Radiation, 26(3), 839-853. https://doi.org/10.1107/s1600577519003448

We designed a convolutional neural network to quickly and accurately upscale the sinograms of x-ray tomograms captured with a low number of projections; effectively increasing the number of projections. This is particularly useful for tomograms that... Read More about A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram.

Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling (2019)
Journal Article
Gibbs, J., French, A., Murchie, E., Wells, D., Pound, M., & Pridmore, T. (2020). Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(6), 1907-1917. https://doi.org/10.1109/TCBB.2019.2896908

Plant phenotyping is the quantitative description of a plant’s physiological, biochemical and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based... Read More about Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling.