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Root metaxylem area influences drought tolerance and transpiration in pearl millet in a soil texture dependent manner (2024)
Preprint / Working Paper
Affortit, P., Faye, A., Jones, D. H., Benson, E., Sine, B., Burridge, J., Ndoye, M. S., Barry, L., Moukouanga, D., Barnard, S., Bhosale, R., Pridmore, T., Gantet, P., Vadez, V., Cubry, P., Kane, N., Bennett, M., Atkinson, J. A., Laplaze, L., Wells, D. M., & Grondin, A. (2024). Root metaxylem area influences drought tolerance and transpiration in pearl millet in a soil texture dependent manner

Pearl millet is a key cereal for food security in drylands but its yield is strongly impacted by drought. We investigated how root anatomical traits contribute to mitigating the effects of vegetative drought stress in pearl millet.

We examined ass... Read More about Root metaxylem area influences drought tolerance and transpiration in pearl millet in a soil texture dependent manner.

Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models (2021)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models. Scientific Reports, 11(1), Article 23279. https://doi.org/10.1038/s41598-021-02466-x

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is mu... Read More about Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models.

A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms (2021)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms. Machine Vision and Applications, 32(3), Article 75. https://doi.org/10.1007/s00138-021-01196-4

Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available.... Read More about A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms.

Hydrodynamic characterization of soil compaction using integrated electrical resistivity and X-ray computed tomography (2021)
Journal Article
Cimpoiasu, M. O., Kuras, O., Wilkinson, P., Pridmore, T., & Mooney, S. J. (2021). Hydrodynamic characterization of soil compaction using integrated electrical resistivity and X-ray computed tomography. Vadose Zone Journal, 20(4), Article e20109. https://doi.org/10.1002/vzj2.20109

© 2020 The Authors. Vadose Zone Journal published by Wiley Periodicals LLC on behalf of Soil Science Society of America Modern agricultural practices can cause significant stress on soil, which ultimately has degrading effects, such as compaction. Th... Read More about Hydrodynamic characterization of soil compaction using integrated electrical resistivity and X-ray computed tomography.

Low-cost automated vectors and modular environmental sensors for plant phenotyping (2020)
Journal Article
Bagley, S. A., Atkinson, J. A., Hunt, H., Wilson, M. H., Pridmore, T. P., & Wells, D. M. (2020). Low-cost automated vectors and modular environmental sensors for plant phenotyping. Sensors, 20(11), Article 3319. https://doi.org/10.3390/s20113319

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increase... Read More about Low-cost automated vectors and modular environmental sensors for plant phenotyping.

Potential of geoelectrical methods to monitor root zone processes and structure: A review (2020)
Journal Article
Cimpoiasu, M. O., Kuras, O., Pridmore, T., & Mooney, S. J. (2020). Potential of geoelectrical methods to monitor root zone processes and structure: A review. Geoderma, 365, 114232. https://doi.org/10.1016/j.geoderma.2020.114232

© 2020 Understanding the processes that control mass and energy exchanges between soil, plants and the atmosphere plays a critical role for understanding the root zone system, but it is also beneficial for practical applications such as sustainable a... Read More about Potential of geoelectrical methods to monitor root zone processes and structure: A review.

CNN-Based Cassava Storage Root Counting Using Real and Synthetic Images (2019)
Journal Article
Atanbori, J., Montoya, M., Selvaraj, M., French, A. P., & Pridmore, T. P. (2019). CNN-Based Cassava Storage Root Counting Using Real and Synthetic Images. Frontiers in Plant Science, 10, Article 1516. https://doi.org/10.3389/fpls.2019.01516

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots a... Read More about CNN-Based Cassava Storage Root Counting Using Real and Synthetic Images.

A low-cost aeroponic phenotyping system for storage root development: Unravelling the below-ground secrets of cassava (Manihot esculenta) (2019)
Journal Article
Selvaraj, M. G., Montoya-P, M. E., Atanbori, J., French, A. P., & Pridmore, T. (2019). A low-cost aeroponic phenotyping system for storage root development: Unravelling the below-ground secrets of cassava (Manihot esculenta). Plant Methods, 15(1), Article 131. https://doi.org/10.1186/s13007-019-0517-6

© 2019 The Author(s). Background: Root and tuber crops are becoming more important for their high source of carbohydrates, next to cereals. Despite their commercial impact, there are significant knowledge gaps about the environmental and inherent reg... Read More about A low-cost aeroponic phenotyping system for storage root development: Unravelling the below-ground secrets of cassava (Manihot esculenta).

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

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 a plant's ability to take up water and nutrients. Segmentat... Read More about RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

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".

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.

Deep Hourglass for Brain Tumor Segmentation (2019)
Book Chapter
Benson, E., Pound, M. P., French, A. P., Jackson, A. S., & Pridmore, T. P. (2019). Deep Hourglass for Brain Tumor Segmentation. In BrainLes 2018: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (419-428). Springer. https://doi.org/10.1007/978-3-030-11726-9_37

The segmentation of a brain tumour in an MRI scan is a challenging task, in this paper we present our results for this problem via the BraTS 2018 challenge, consisting of 210 high grade glioma (HGG) and 75 low grade glioma (LGG) volumes for training.... Read More about Deep Hourglass for Brain Tumor Segmentation.

Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction (2018)
Journal Article
Gibbs, J., Pound, M., French, A. P., Wells, D. M., Murchie, E., & Pridmore, T. (2018). Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction. Plant Physiology, 178(2), 524-534. https://doi.org/10.1104/pp.18.00664

© 2018 American Society of Plant Biologists. All rights reserved. Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modeling. However, the co... Read More about Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction.

Erratum: Author Correction: Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate (Nature communications (2018) 9 1 (1408)) (2018)
Journal Article
Giri, J., Bhosale, R., Huang, G., Pandey, B. K., Parker, H., Zappala, S., …Bennett, M. J. (2018). Erratum: Author Correction: Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate (Nature communications (2018) 9 1 (1408)). Nature Communications, 9(1), Article 1810. https://doi.org/10.1038/s41467-018-04280-y

The original version of this Article omitted the following from the Acknowledgements:'We also thank DBT-CREST BT/HRD/03/01/2002.'This has been corrected in both the PDF and HTML versions of the Article.

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.

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.