Pouria Sadeghi-Tehran

NamePouria Sadeghi-Tehran
Job titleResearch Scientist - Field Phenotyping
Email addresspouria.sadeghi-tehran@rothamsted.ac.uk
DepartmentPlant Sciences
Research clusterPS: Crop Productivity and Quality
ORCIDhttps://orcid.org/0000-0003-0352-227X
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.

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.

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.

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.

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.

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).

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.

353 total views of outputs
189 total downloads of outputs
20 views of outputs this month
5 downloads of outputs this month