Biostatistics

The research group focuses on applied statistics within plant phenotyping, agronomy, and ecotoxicology.

Trends data with floating drone

We work closely together with Nordic plant breeders on developing and accessing tools and methodology for plant phenotyping using drone data.

We develop and optimize designs for agronomic experiments and evaluate the reproducibility of data obtained from different sources and or under different conditions. Part of our work consist in developing R-packages for easier accessibility for the end-user.  

 

  • Benchmark dose methodology
  • Dose-response models
  • Plant phenotyping
  • Design of experiment
  • Linear mixed models
  • Simultaneous inference

The aim is to advance, extend and enhance statistical methodology specifically within toxicophenomics with the aim of optimizing the use of high-throughput plant phenotyping in advanced environmental toxicological risk assessment. Also, the aim is to extend the research group with more data science experience towards an increased utilization of deep learning, e.g., neural networks, for image processing for trait recognition and/or plant organ counting.

 

The MATRIX: Microbiome Assisted Triticum Resilience In X-dimensions, funded by the Novo Nordisk Foundation

6P3: Nordic Public Private Partnership Plant Phenotyping Project. Phenotyping in a breeding context with several Nordic private breeding companies and universities https://nordicphenotyping.org/, funded by NordGen

AnaEE Denmark: a Danish infrastructure for experimental ecosystem research funded by the Danish Ministry of Science and Education

Halm til det hele: Phenotyping the straw. Funded by Promilleafgiftsfonden for landbrug

Enhancing statistical methodology for toxicophenomics: High-throughput and high-dimensional data for ecotoxicological risk assessment. Funded by the Novo Nordisk Foundation

WeLaser: Weed control using image processing, laser and robot technologies. EU project under Horizon 2020

 

Christensen S, Dyrmann N, Laursen MS, Jørgensen RN and Rasmussen J (2021) Sensing for Weed Detection. Sensing Approaches for Precision Agriculture. Springer, Cham. 275-300.

Hawkes C, Kjøller R, Raaijmakers JM, Riber L, Christensen S, Rasmussen S, Christensen JH, Dahl AB, Westergaard JC, Nielsen M, Brown-Guedira G and Hansen LH. (2021) Extension of Plant Phenotypes by the Foliar Microbiome, Annual Review of Plant Biology 72:15.1–15.24.

Jensen SM, Kluxen FM, Ritz C (2021). Benchmark dose modelling in regulatory ecotoxicology, a potential tool in pest management. Pest Management Science. 78(5), 1772-1779

Jensen SM, Akhter MJ, Azim S, Rasmussen J (2021). The predictive power of regression models to determine grass weed infestations in cereals based on drone imagery – statistical and practical aspects. Agronomy. 11(11), 2277

Jensen SM, Cedergreen N, Kluxen FM, Ritz C (2021). A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data. Risk Analysis. 41(11), 2081-2093

 

 

 

 

 

 

Group members

Name Title Phone E-mail
David Redek Academic Research Staff +4535321473 E-mail
Jens Baalkilde Andersen PhD Student +4535328847 E-mail
Signe Marie Jensen Associate Professor +4535333431 E-mail
Svend Christensen Head of Department +4551489421 E-mail

MsC students

  • Federico Calamita
  • Marta Ákadóttir

Research group leader

Signe Marie Jensen
Associate Professor
smj@plen.ku.dk
+4535333431