Outputs (25)

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.

X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil (2022)
Journal Article
Griffiths, M., Mellor, N., Sturrock, C. J., Atkinson, B. S., Johnson, J., Mairhofer, S., York, L. M., Atkinson, J. A., Soltaninejad, M., Foulkes, J. F., Pound, M. P., Mooney, S. J., Pridmore, T. P., Bennett, M. J., & Wells, D. M. (2022). X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil. Plant Phenome Journal, 5(1), Article e20036. https://doi.org/10.1002/ppj2.20036

The spatial arrangement of the root system, termed root system architecture, is important for resource acquisition as it directly affects the soil zone explored. Methods for phenotyping roots are mostly destructive, which prevents analysis of roots o... Read More about X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil.

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.

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.

Towards infield, live plant phenotyping using a reduced-parameter CNN (2019)
Journal Article
Atanbori, J., French, A. P., & Pridmore, T. P. (2020). Towards infield, live plant phenotyping using a reduced-parameter CNN. Machine Vision and Applications, 31, Article 2. https://doi.org/10.1007/s00138-019-01051-7

There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping re- search to help with the breeding of high yieldi... Read More about Towards infield, live plant phenotyping using a reduced-parameter CNN.

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".

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., Yang, J., Dievart, A., Bureau, C., Ljung, K., Price, A., Rose, T., Larrieu, A., Mairhofer, S., Sturrock, C. J., White, P., Dupuy, L., Hawkesford, M., Perin, C., Liang, W., …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.

Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate (2018)
Journal Article
Giri, J., Bhosale, R., Huang, G., Pandey, B. K., Parker, H., Zappala, S., Yang, J., Dievart, A., Bureau, C., Ljung, K., Price, A., Rose, T., Larrieu, A., Mairhofer, S., Sturrock, C. J., White, P., Dupuy, L., Hawkesford, M., Perin, C., Liang, W., …Bennett, M. J. (2018). Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate. Nature Communications, 9(1), https://doi.org/10.1038/s41467-018-03850-4

Root traits such as root angle and hair length influence resource acquisition particularly for immobile nutrients like phosphorus (P). Here, we attempted to modify root angle in rice by disrupting the OsAUX1 auxin influx transporter gene in an effort... Read More about Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate.

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. Deep learning for multi-task plant phenotyping. Presented at ICCV 2017 International Conference on Computer Vision, Venice, Italy

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.