Publications

Scientific papers on application cases for LemnaTec technologies

Applications of LemnaTec hard- and software in plant phenotyping, seedling assessments, ecotoxicology, and more are reported in hundreds of publications. Scientific papers describe application cases for imaging and image processing with many plant species, but also with non-plant organisms.

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  • Wild, Andreas J.; Steiner, Franziska A.; Kiene, Marvin; Tyborski, Nicolas; Tung, Shu-Yin; Koehler, Tina; Carminati, Andrea; Eder, Barbara; Groth, Jennifer; Vahl, Wouter K.; Wolfrum, Sebastian; Lueders, Tillmann; Laforsch, Christian; Mueller, Carsten W.; Vidal, Alix; Pausch, Johanna (2024)
    Unraveling root and rhizosphere traits in temperate maize landraces and modern cultivars: Implications for soil resource acquisition and drought adaptation
    Plant Cell Environ - 47(7) 2526–2541, DOI: 10.1111/pce.14898

  • Genze, Nikita; Vahl, Wouter K.; Groth, Jennifer; Wirth, Maximilian; Grieb, Michael; Grimm, Dominik G. (2024)
    Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision
    Scientific Data - 11(1) 109, DOI: 10.1038/s41597-024-02945-6

  • Khan, Nazifa Azam; Cieslak, Mikolaj; McQuillan, Ian (2024)
    Importance of realism in procedurally-generated synthetic images for deep learning: case studies in maize and canola
    - () , DOI: 10.48550/arXiv.2404.05128

  • Steiner, Franziska A.; Wild, Andreas J.; Tyborski, Nicolas; Tung, Shu-Yin; Koehler, Tina; Buegger, Franz; Carminati, Andrea; Eder, Barbara; Groth, Jennifer; Hesse, Benjamin D.; Pausch, Johanna; Lüders, Tillmann; Vahl, Wouter K.; Wolfrum, Sebastian; Mueller, Carsten W.; Vidal, Alix (2024)
    Rhizosheath drought responsiveness is variety-specific and a key component of belowground plant adaptation
    New Phytol (New Phytologist) - 242(2) 479–492, DOI: 10.1111/nph.19638

  • Xu, Zheng; Wu, Cong (2023)
    Combination of Transfer Deep Learning and Classical Machine Learning Models for Multi-View Image Analysis
    - () 13, DOI: 10.3390/IOCMA2023-14401

  • Thapa, Kantilata (2023)
    Characterization of Physical and Biochemical Traits in Wheat and Corn Plants Using High Throughput Image Analysis
    Thesis, University of Nebraska Lincoln - () , DOI:

  • Wang, Weixuan; Guo, Weijun; Le, Liang; Yu, Jia; Wu, Yue; Li, Dongwei; Wang, Yifan; Wang, Huan; Lu, Xiaoduo; Qiao, Hong; Gu, Xiaofeng; Tian, Jian; Zhang, Chunyi; Pu, Li (2023)
    Integration of high-throughput phenotyping, GWAS, and predictive models reveals the genetic architecture of plant height in maize
    Molecular Plant - 16(2) 354–373, DOI: 10.1016/j.molp.2022.11.016

  • Ramírez-Rojas, Christian; Peña-Valdivia, Cecilia Beatriz; García-Esteva, Antonio; Padilla-Chacón, Daniel (2022)
    Fenotipo de plantas de maíz con efecto del herbicida mesotrione
    Remexca (Revista Mexicana de Ciencias Agrícolas) - 13(8) 1399–1410, DOI: 10.29312/remexca.v13i8.2886

  • Tung, Shu-Yin; Köhler, Tina; Wild, Andreas J.; Steiner, Franziska; Tyborski, Nicolas; Pausch, Johanna; Lüders, Tillmann; Müller, Carsten W.; Vidal, Alix; Carminati, Andrea; Vahl, Wouter; Groth, Jennifer; Eder, Barbara; Wolfrum, Sebastian (2022)
    High-throughput phenotyping of 38 maize varieties for the study of rhizosphere traits affecting agronomic resilience under drought stress 
    - () , DOI: 10.5194/egusphere-egu22-4020

  • Rossi, Riccardo; Costafreda-Aumedes, Sergi; Summerer, Stephan; Moriondo, Marco; Leolini, Luisa; Cellini, Francesco; Bindi, Marco; Petrozza, Angelo (2022)
    A comparison of high-throughput imaging methods for quantifying plant growth traits and estimating above-ground biomass accumulation
    European Journal of Agronomy - 141() 126634, DOI: 10.1016/j.eja.2022.126634