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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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