Outputs (25)

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.

Supporting data for "Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping" (2016)
Data
(2016). Supporting data for "Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping". [Data]. https://doi.org/10.5524/100343

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; hence the motivation... Read More about Supporting data for "Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping".

The 4-Dimensional Plant: Effects of Wind-Induced Canopy Movement on Light Fluctuations and Photosynthesis (2016)
Journal Article
Burgess, A. J., Retkute, R., Preston, S. P., Jensen, O. E., Pound, M. P., Pridmore, T. P., & Murchie, E. H. (2016). The 4-Dimensional Plant: Effects of Wind-Induced Canopy Movement on Light Fluctuations and Photosynthesis. Frontiers in Plant Science, 7(1392), https://doi.org/10.3389/fpls.2016.01392

Physical perturbation of a plant canopy brought about by wind is a ubiquitous phenomenon and yet its biological importance has often been overlooked. This is partly due to the complexity of the issue at hand: wind-induced movement (or mechanical exci... Read More about The 4-Dimensional Plant: Effects of Wind-Induced Canopy Movement on Light Fluctuations and Photosynthesis.

Approaches to three-dimensional reconstruction of plant shoot topology and geometry (2016)
Journal Article
Gibbs, J., Pound, M. P., French, A. P., Wells, D. M., Murchie, E. H., & Pridmore, T. P. (2016). Approaches to three-dimensional reconstruction of plant shoot topology and geometry. Functional Plant Biology, 44(1), 62-75. https://doi.org/10.1071/FP16167

There are currently 805 million people classified as chronically undernourished, and yet the World’s population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and... Read More about Approaches to three-dimensional reconstruction of plant shoot topology and geometry.

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.

A patch-based approach to 3D plant shoot phenotyping (2016)
Journal Article
Pound, M. P., French, A. P., Fozard, J. A., Murchie, E. H., & Pridmore, T. P. (2016). A patch-based approach to 3D plant shoot phenotyping. Machine Vision and Applications, 27(5), 767-779. https://doi.org/10.1007/s00138-016-0756-8

The emerging discipline of plant phenomics aims to measure key plant characteristics, or traits, though as yet the set of plant traits that should be measured by automated systems is not well defined. Methods capable of recovering generic representat... Read More about A patch-based approach to 3D plant shoot phenotyping.

Three-dimensional reconstruction of plant shoots from multiple images using an active vision system (2015)
Journal Article
Gibbs, J., Pound, M. P., Wells, D. M., Murchie, E. H., French, A. P., & Pridmore, T. P. (2015). Three-dimensional reconstruction of plant shoots from multiple images using an active vision system

The reconstruction of 3D models of plant shoots is a challenging problem central to the emerging discipline of plant phenomics – the quantitative measurement of plant structure and function. Current approaches are, however, often limited by the use o... Read More about Three-dimensional reconstruction of plant shoots from multiple images using an active vision system.

Visual tracking for the recovery of multiple interacting plant root systems from X-ray μCT images (2015)
Journal Article
Mairhofer, S., Johnson, J., Sturrock, C., Bennett, M. J., Mooney, S. J., & Pridmore, T. P. (2016). Visual tracking for the recovery of multiple interacting plant root systems from X-ray μCT images. Machine Vision and Applications, 27(5), 721-734. https://doi.org/10.1007/s00138-015-0733-7

We propose a visual object tracking framework for the extraction of multiple interacting plant root systems from three-dimensional X-ray micro computed tomography images of plants grown in soil. Our method is based on a level set framework guided by... Read More about Visual tracking for the recovery of multiple interacting plant root systems from X-ray μCT images.

Extracting multiple interacting root systems using X-ray microcomputed tomography (2015)
Journal Article
Mairhofer, S., Sturrock, C., Mooney, S. J., Pridmore, T. P., & Bennett, M. J. (2015). Extracting multiple interacting root systems using X-ray microcomputed tomography. Plant Journal, 84(5), 1034-1043. https://doi.org/10.1111/tpj.13047

© 2015 The Authors The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd. Root system interactions and competition for resources are active areas of research that contribute to our understanding of how roots perc... Read More about Extracting multiple interacting root systems using X-ray microcomputed tomography.