Image analysis for plant phenotyping - machine learning based methods for analysis of multi-model and multi-dimensional remote sensing data from high-throughput plant phenotyping
Repository project
| Project dates | 01 Nov 2019 to end of 31 Oct 2023 |
|---|---|
| Researchers | |
| Principal Investigator | Malcolm Hawkesford |
| Funder | Office Chérifien des Phosphate (OCP) |
| Project number | RP10519-10 |
| Department | Sustainable Soils and Crops |
| Participating organisation | Rothamsted Research |
Outputs
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Hyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modeling and machine learning techniques in wheat
A - Papers appearing in refereed journals
Okyere, F., Cudjoe, D., Virlet, N., Castle, M., Riche, A. B., Greche, L., Mohareb, F., Simms, D., Mhada, M. and Hawkesford, M. J. 2024. Hyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modeling and machine learning techniques in wheat. Remote Sensing. 16 (18), p. 3446. https://doi.org/10.3390/rs16183446
Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods
A - Papers appearing in refereed journals
Okyere, F., Cudjoe, D., Sadeghi-Tehran, P., Virlet, N., Riche, A. B., Castle, M., Greche, L., Simms, D., Mhada, M., Mohareb, F. and Hawkesford, M. J. 2023. Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods. Frontiers in Plant Science. 14, p. 1209500. https://doi.org/10.3389/fpls.2023.1209500
Machine Learning Methods for Automatic Segmentation of Images of Field and Glasshouse Based Plants for High Throughput Phenotyping
A - Papers appearing in refereed journals
Okyere, F., Cudjoe, D., Sadeghi-Tehran, P., Virlet, N., Riche, A. B., Castle, M., Greche, L., Mohareb, F., Simms, D. M., Mhada, M. and Hawkesford, M. J. 2023. Machine Learning Methods for Automatic Segmentation of Images of Field and Glasshouse Based Plants for High Throughput Phenotyping. Plants - Basel. 12 (10), p. 2035. https://doi.org/10.3390/plants12102035
Using proximal sensing parameters linked to the photosynthetic capacity to assess the nutritional status and yield potential in quinoa
A - Papers appearing in refereed journals
Cudjoe, D., Okyere, F., Virlet, N., Castle, M., Buchner, P. H., Parmar, S., Sadeghi-Tehran, P., Riche, A. B., Sohail, Q., Mhada, M., Ghanem, M., Waine, T. W., Mohareb, F. and Hawkesford, M. J. 2023. Using proximal sensing parameters linked to the photosynthetic capacity to assess the nutritional status and yield potential in quinoa. Acta Horticulturae. 1360, pp. 373-379. https://doi.org/10.17660/ActaHortic.2023.1360.45