Outputs (3)

Deep learning for multi-task plant phenotyping (2017)
Presentation / Conference Contribution
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 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.

Quantification of root water uptake in soil using X-ray Computed Tomography and image based modelling (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. 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.