| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 2170 | 2018 |
| Deep machine learning provides state-of-the-art performance in image-based plant phenotyping MP Pound, JA Atkinson, AJ Townsend, MH Wilson, M Griffiths, ... Gigascience 6 (10), gix083, 2017 | 401* | 2017 |
| Uncovering the hidden half of plants using new advances in root phenotyping JA Atkinson, MP Pound, MJ Bennett, DM Wells Current opinion in biotechnology 55, 1-8, 2019 | 360 | 2019 |
| RootNav: navigating images of complex root architectures MP Pound, AP French, JA Atkinson, DM Wells, MJ Bennett, T Pridmore Plant physiology 162 (4), 1802-1814, 2013 | 275 | 2013 |
| Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat JA Atkinson, LU Wingen, M Griffiths, MP Pound, O Gaju, MJ Foulkes, ... Journal of Experimental Botany 66 (8), 2283-2292, 2015 | 259 | 2015 |
| Systems Analysis of Auxin Transport in the Arabidopsis Root Apex LR Band, DM Wells, JA Fozard, T Ghetiu, AP French, MP Pound, ... The Plant Cell 26 (3), 862-875, 2014 | 232 | 2014 |
| Deep learning for multi-task plant phenotyping MP Pound, JA Atkinson, DM Wells, TP Pridmore, AP French Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 217 | 2017 |
| Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields J Gao, AP French, MP Pound, Y He, TP Pridmore, JG Pieters Plant methods 16, 1-12, 2020 | 188 | 2020 |
| Automated recovery of three-dimensional models of plant shoots from multiple color images MP Pound, AP French, EH Murchie, TP Pridmore Plant physiology 166 (4), 1688-1698, 2014 | 159 | 2014 |
| Root system markup language: toward a unified root architecture description language G Lobet, MP Pound, J Diener, C Pradal, X Draye, C Godin, M Javaux, ... Plant physiology 167 (3), 617-627, 2015 | 141 | 2015 |
| RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures R Yasrab, JA Atkinson, DM Wells, AP French, TP Pridmore, MP Pound GigaScience 8 (11), giz123, 2019 | 128 | 2019 |
| Integration of hormonal signaling networks and mobile microRNAs is required for vascular patterning in Arabidopsis roots D Muraro, N Mellor, MP Pound, H Help, M Lucas, J Chopard, HM Byrne, ... Proceedings of the National Academy of Sciences 111 (2), 857-862, 2014 | 114 | 2014 |
| Approaches to three-dimensional reconstruction of plant shoot topology and geometry JA Gibbs, M Pound, AP French, DM Wells, E Murchie, T Pridmore Functional Plant Biology 44 (1), 62-75, 2016 | 113 | 2016 |
| CellSeT: novel software to extract and analyze structured networks of plant cells from confocal images MP Pound, AP French, DM Wells, MJ Bennett, TP Pridmore The Plant Cell 24 (4), 1353-1361, 2012 | 98 | 2012 |
| The 6xABRE Synthetic Promoter Enables the Spatiotemporal Analysis of ABA-Mediated Transcriptional Regulation R Wu, L Duan, JL Pruneda-Paz, D Oh, M Pound, S Kay, JR Dinneny Plant Physiology 177 (4), 1650-1665, 2018 | 93 | 2018 |
| The 4-dimensional plant: effects of wind-induced canopy movement on light fluctuations and photosynthesis AJ Burgess, R Retkute, SP Preston, OE Jensen, MP Pound, TP Pridmore, ... Frontiers in plant science 7, 1392, 2016 | 88 | 2016 |
| High-resolution three-dimensional structural data quantify the impact of photoinhibition on long-term carbon gain in wheat canopies in the field AJ Burgess, R Retkute, MP Pound, J Foulkes, SP Preston, OE Jensen, ... Plant Physiology 169 (2), 1192-1204, 2015 | 72 | 2015 |
| Predicting plant growth from time-series data using deep learning R Yasrab, J Zhang, P Smyth, MP Pound Remote Sensing 13 (3), 331, 2021 | 71 | 2021 |
| Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction JA Gibbs, M Pound, AP French, DM Wells, E Murchie, T Pridmore Plant physiology 178 (2), 524-534, 2018 | 63 | 2018 |
| A review of ultrasonic sensing and machine learning methods to monitor industrial processes AL Bowler, MP Pound, NJ Watson Ultrasonics 124, 106776, 2022 | 48 | 2022 |