All Outputs (5)

An updated protocol for high throughput plant tissue sectioning (2017)
Journal Article
Atkinson, J. A., & Wells, D. M. (2017). An updated protocol for high throughput plant tissue sectioning. Frontiers in Plant Science, 8, https://doi.org/10.3389/fpls.2017.01721

Quantification of the tissue and cellular structure of plant material is essential for the study of a variety of plant sciences applications. Currently, many methods for sectioning plant material are either low throughput or involve free-hand section... Read More about An updated protocol for high throughput plant tissue sectioning.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies (2017)
Journal Article
Atkinson, J. A., Lobet, G., Noll, M., Meyer, P. E., Griffiths, M., & Wells, D. M. (2017). Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies. GigaScience, 6(10), https://doi.org/10.1093/gigascience/gix084

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a... Read More about Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping (2017)
Journal Article
Pound, M. P., Atkinson, J. A., Townsend, A. J., Wilson, M. H., Griffiths, M., Jackson, A. S., …French, A. P. (2017). Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience, 6(10), Article gix083. https://doi.org/10.1093/gigascience/gix083

© The Author 2017. 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, h... Read More about Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

Linear discriminant analysis reveals differences in root architecture in wheat seedlings related to nitrogen uptake efficiency (2017)
Journal Article
Kenobi, K., Atkinson, J. A., Wells, D. M., Gaju, O., deSilva, J. G., Foulkes, M. J., …Bennett, M. J. (2017). Linear discriminant analysis reveals differences in root architecture in wheat seedlings related to nitrogen uptake efficiency. Journal of Experimental Botany, 68(17), 4969-4981. https://doi.org/10.1093/jxb/erx300

© The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. Root architecture impacts water and nutrient uptake efficiency. Identifying exactly which root architectural properties influence these agronom... Read More about Linear discriminant analysis reveals differences in root architecture in wheat seedlings related to nitrogen uptake efficiency.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies (2017)
Other
Atkinson, J. A., Lobet, G., Noll, M., Meyer, P. E., Griffiths, M., & Wells, D. M. (2017). Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

Background: Genetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to e... Read More about Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies.