| Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring N Virlet, K Sabermanesh, P Sadeghi-Tehran, MJ Hawkesford Functional Plant Biology 44 (1), 143-153, 2017 | 383 | 2017 |
| Field phenotyping of water stress at tree scale by UAV-sensed imagery: new insights for thermal acquisition and calibration D Gómez-Candón, N Virlet, S Labbé, A Jolivot, JL Regnard Precision agriculture 17, 786-800, 2016 | 135 | 2016 |
| DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks P Sadeghi-Tehran, N Virlet, EM Ampe, P Reyns, MJ Hawkesford Frontiers in Plant Science 10, 1176, 2019 | 125 | 2019 |
| Automated method to determine two critical growth stages of wheat: heading and flowering P Sadeghi-Tehran, K Sabermanesh, N Virlet, MJ Hawkesford Frontiers in Plant Science 8, 233406, 2017 | 123 | 2017 |
| Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping P Sadeghi-Tehran, N Virlet, K Sabermanesh, MJ Hawkesford Plant methods 13, 1-16, 2017 | 57 | 2017 |
| Stress indicators based on airborne thermal imagery for field phenotyping a heterogeneous tree population for response to water constraints N Virlet, V Lebourgeois, S Martinez, E Costes, S Labbé, JL Regnard Journal of experimental botany 65 (18), 5429-5442, 2014 | 53 | 2014 |
| Multispectral airborne imagery in the field reveals genetic determinisms of morphological and transpiration traits of an apple tree hybrid population in response to water deficit N Virlet, E Costes, S Martinez, JJ Kelner, JL Regnard Journal of experimental botany 66 (18), 5453-5465, 2015 | 45 | 2015 |
| Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform DH Lyra, N Virlet, P Sadeghi-Tehran, KL Hassall, LU Wingen, S Orford, ... Journal of Experimental Botany 71 (6), 1885-1898, 2020 | 38 | 2020 |
| Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology P Sadeghi-Tehran, P Angelov, N Virlet, MJ Hawkesford Journal of Imaging 5 (3), 33, 2019 | 27 | 2019 |
| A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery P Sadeghi-Tehran, N Virlet, MJ Hawkesford Remote Sensing 13 (5), 898, 2021 | 14 | 2021 |
| High resolution thermal and multispectral UAV imagery for precision assessment of apple tree response to water stress D Gomez-Candon, S Labbé, N Virlet, A Jolivot, JL Regnard 2. International Conference on Robotics and associated High-technologies and …, 2014 | 11 | 2014 |
| Time‐intensive geoelectrical monitoring under winter wheat G Blanchy, N Virlet, P Sadeghi‐Tehran, CW Watts, MJ Hawkesford, ... Near Surface Geophysics 18 (Geoelectrical Monitoring), 413-425, 2020 | 8 | 2020 |
| Digital field phenotyping by LemnaTec T Dornbusch, M Hawkesford, M Jansen, K Nagel, B Niehaus, S Paulus, ... Aachen: LemnaTec, 2015 | 7 | 2015 |
| Contribution of high-resolution remotely sensed thermal-infrared imagery to high-throughput field phenotyping of an apple progeny submitted to water constraints N Virlet, D Gomez-Candon, V Lebourgeois, S Martinez, A Jolivot, PE Lauri, ... XXIX International Horticultural Congress on Horticulture: Sustaining Lives …, 2014 | 5 | 2014 |
| Contribution of airborne remote sensing to high-throughput phenotyping of a hybrid apple population in response to soil water constraints N Virlet, S Martinez, V Lebourgeois, S Labbé, JL Regnard Options Méditerranéennes. Série B: Etudes et Recherches, 185-191, 2012 | 4 | 2012 |
| Rothamsted Research N Virlet United Kingdom, 0 | 4 | |
| Machine Learning Methods for Automatic Segmentation of Images of Field-and Glasshouse-Based Plants for High-Throughput Phenotyping FG Okyere, D Cudjoe, P Sadeghi-Tehran, N Virlet, AB Riche, M Castle, ... Plants 12 (10), 2035, 2023 | 3 | 2023 |
| DeepCount: in-field automatic quantification of wheat spikes using simple linear iterative clustering and deep convolutional neural networks ST Pouria, N Virlet, EM Ampe, P Reyns, MJ Hawkesford Front Plant Sci 10, 1176, 2019 | 3 | 2019 |
| Phenotyping the response of an apple tree hybrid population to soil water constraint under field conditions: new insights brought by high resolution imaging N Virlet, V Lebourgeois, S Martinez, S Labbé, E Costes, JL Regnard II International Symposium on Horticulture in Europe 1099, 879-886, 2012 | 3 | 2012 |
| Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods FG Okyere, D Cudjoe, P Sadeghi-Tehran, N Virlet, AB Riche, M Castle, ... Frontiers in Plant Science 14, 1209500, 2023 | 2 | 2023 |
Malcolm HawkesfordRothamsted ResearchVerified email at rothamsted.ac.uk
