| The use of plant models in deep learning: an application to leaf counting in rosette plants J Ubbens, M Cieslak, P Prusinkiewicz, I Stavness Plant methods 14, 1-10, 2018 | 271 | 2018 |
| Quasi-Monte Carlo simulation of the light environment of plants M Cieslak, C Lemieux, J Hanan, P Prusinkiewicz Functional Plant Biology 35 (10), 837-849, 2008 | 92 | 2008 |
| A functional–structural kiwifruit vine model integrating architecture, carbon dynamics and effects of the environment M Cieslak, AN Seleznyova, J Hanan Annals of Botany 107 (5), 747-764, 2011 | 88 | 2011 |
| Plasticity influencing the light compensation point offsets the specialization for light niches across shrub species in a tropical forest understorey FJ Sterck, RA Duursma, RW Pearcy, F Valladares, M Cieslak, ... Journal of Ecology 101 (4), 971-980, 2013 | 63 | 2013 |
| Stochastic P systems and the simulation of biochemical processes with dynamic compartments A Spicher, O Michel, M Cieslak, JL Giavitto, P Prusinkiewicz BioSystems 91 (3), 458-472, 2008 | 62 | 2008 |
| Auxin-driven patterning with unidirectional fluxes M Cieslak, A Runions, P Prusinkiewicz Journal of experimental botany 66 (16), 5083-5102, 2015 | 58 | 2015 |
| Towards aspect-oriented functional–structural plant modelling M Cieslak, AN Seleznyova, P Prusinkiewicz, J Hanan Annals of Botany 108 (6), 1025-1041, 2011 | 53 | 2011 |
| Latent space phenotyping: automatic image-based phenotyping for treatment studies J Ubbens, M Cieslak, P Prusinkiewicz, I Parkin, J Ebersbach, I Stavness Plant Phenomics, 2020 | 44 | 2020 |
| Phyllotactic patterning of gerbera flower heads T Zhang, M Cieslak, A Owens, F Wang, SK Broholm, TH Teeri, P Elomaa, ... Proceedings of the National Academy of Sciences 118 (13), e2016304118, 2021 | 42 | 2021 |
| NMR investigation of functionalized magnetic nanoparticles Fe3O4 as T1–T2 contrast agents S Kenouche, J Larionova, N Bezzi, Y Guari, N Bertin, M Zanca, L Lartigue, ... Powder technology 255, 60-65, 2014 | 39 | 2014 |
| Modeling dense inflorescences A Owens, M Cieslak, J Hart, R Classen-Bockhoff, P Prusinkiewicz ACM Transactions on Graphics (TOG) 35 (4), 1-14, 2016 | 34 | 2016 |
| Modeling plant development with L-systems P Prusinkiewicz, M Cieslak, P Ferraro, J Hanan Mathematical modelling in plant biology, 139-169, 2018 | 31 | 2018 |
| RandQMC user’s guide: A package for randomized quasi-Monte Carlo methods in C C Lemieux, M Cieslak, K Luttmer Technical Rep. 2002-712 15, 2002 | 26 | 2002 |
| Integrating physiology and architecture in models of fruit expansion M Cieslak, I Cheddadi, F Boudon, V Baldazzi, M Génard, C Godin, ... Frontiers in plant science 7, 1739, 2016 | 24 | 2016 |
| Computational models of auxin-driven patterning in shoots M Cieslak, A Owens, P Prusinkiewicz Cold Spring Harbor Perspectives in Biology 14 (3), a040097, 2022 | 19 | 2022 |
| RandQMC user’s guide: a package for randomized Quasi-Monte Carlo methods in C (2004). Software user’s guide C Lemieux, M Cieslak, K Luttmer | 17 | |
| A functional-structural kiwifruit vine model M Cieslak, AN Seleznyova, J Hanan 2009 Third international symposium on plant growth modeling, simulation …, 2009 | 14 | 2009 |
| Modeling flower pigmentation patterns L Ringham, A Owens, M Cieslak, LD Harder, P Prusinkiewicz ACM Transactions on Graphics (TOG) 40 (6), 1-14, 2021 | 13 | 2021 |
| In vivo quantitative NMR imaging of fruit tissues during growth using Spoiled Gradient Echo sequence S Kenouche, M Perrier, N Bertin, J Larionova, A Ayadi, M Zanca, J Long, ... Magnetic Resonance Imaging 32 (10), 1418-1427, 2014 | 13 | 2014 |
| L-system models for image-based phenomics: case studies of maize and canola M Cieslak, N Khan, P Ferraro, R Soolanayakanahally, SJ Robinson, ... in silico Plants 4 (1), diab039, 2022 | 11 | 2022 |
