Nicolas Virlet

NameNicolas Virlet
Job titlePost Doctoral Research Scientist - Field Phenotyping
Email addressnicolas.virlet@rothamsted.ac.uk
DepartmentPlant Sciences
Research clusterPS: Crop Productivity and Quality
ORCIDhttps://orcid.org/0000-0001-6030-4282
OfficeHarpenden

Research outputs

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

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