Nicolas Virlet

Name	Nicolas Virlet
Job title	Senior Scientific Specialist - Field Phenotyping
Email address	nicolas.virlet@rothamsted.ac.uk
Department	Sustainable Soils and Crops
Research cluster	PS: Crop Productivity and Quality
ORCID	https://orcid.org/0000-0001-6030-4282
Office	Harpenden

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Research outputs

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods

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

Field phenotyping for African crops: overview and perspectives

Cudjoe, D., Virlet, N., Castle, M., Riche, A. B., Mhada, M., Waine, T. W., Mohareb, F. and Hawkesford, M. J. 2023. Field phenotyping for African crops: overview and perspectives . Frontiers in Plant Science. 14, p. 1219673. https://doi.org/10.3389/fpls.2023.1219673

Machine Learning Methods for Automatic Segmentation of Images of Field and Glasshouse Based Plants for High Throughput Phenotyping

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

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

A predictive model of wheat grain yield based on canopy reflectance indices and the theoretical definition of yield potential

Pennacchi, J. P., Virlet, N., Barbosa, J. P. R. A. D., Parry, M. A. J., Feuerhelm, D., Hawkesford, M. J. and Carmo-Silva, E. 2022. A predictive model of wheat grain yield based on canopy reflectance indices and the theoretical definition of yield potential. Theoretical and Experimental Plant Physiology. 34, pp. 537-550. https://doi.org/10.1007/s40626-022-00263-z

Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

Virlet, N., Lyra, D. H. and Hawkesford, M. J. 2022. Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops. in: Walter, A. (ed.) Advances in plant phenotyping for more sustainable crop production Burleigh Dodds.

A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

Sadeghi-Tehran, P., Virlet, N. and Hawkesford, M. J. 2021. A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery. Remote Sensing. 13 (5), p. 898. https://doi.org/10.3390/rs13050898

Time-intensive geoelectrical monitoring under winter wheat

Blanchy, G., Virlet, N., Sadeghi-Tehran, P., Watts, C. W., Hawkesford, M. J., Whalley, W. R. and Binley, A. 2020. Time-intensive geoelectrical monitoring under winter wheat . Near Surface Geophysics. https://doi.org/10.1002/nsg.12107

Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform

Lyra, D. H., Virlet, N., Sadeghi-Tehran, P., Hassall, K. L., Wingen, L. U., Orford, S., Griffiths, S., Hawkesford, M. J. and Slavov, G. 2020. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform . Journal of Experimental Botany. p. erz545. https://doi.org/10.1093/jxb/erz545

DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks

Sadeghi-Tehran, P., Virlet, N., Ampe, E. M., Reyns, P. and Hawkesford, M. J. 2019. DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks. Frontiers in Plant Science. 10, p. 1176. https://doi.org/10.3389/fpls.2019.01176

Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology

Sadeghi-Tehran, P., Angelov, P., Virlet, N. and Hawkesford, M. J. 2019. Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology. Journal of Imaging. 5 (3), p. 33. https://doi.org/10.3390/jimaging5030033

Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

Sadeghi-Tehran, P., Virlet, N., Sabermanesh, K. and Hawkesford, M. J. 2017. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping. Plant Methods. 13 (103), pp. 1-16. https://doi.org/10.1186/s13007-017-0253-8

Automated method to determine two critical growth stages of wheat: heading and flowering

Sadeghi-Tehran, P., Sabermanesh, K., Virlet, N. and Hawkesford, M. J. 2017. Automated method to determine two critical growth stages of wheat: heading and flowering. Frontiers in Plant Science. 8 (252). https://doi.org/10.3389/fpls.2017.00252

Field Scanalyzer: An automated robotic ﬁeld phenotyping platform for detailed crop monitoring

Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P. and Hawkesford, M. J. 2016. Field Scanalyzer: An automated robotic ﬁeld phenotyping platform for detailed crop monitoring. Functional Plant Biology. 44 (1), pp. 143-153. https://doi.org/10.1071/FP16163