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Image-based phenotyping platforms in semi-controlled conditions offer enormous possibilities to perform genetic analyses of plant growth, architecture, light interception and biomass accumulation over large time series for thousands of plants in well-defined environmental scenarios. The objective of this project is to develop an integrated pipeline inside the OpenAlea framework, allowing assessment of growths of individual organs, plant geometry and derived variables such as light interception. The project involves (i) Plant image segmentation allowing computing growths of individual organs under different environmental conditions. (ii) Assessment of architectural variables, their change with time and environment, allowing genetic analysis of these traits (iii) Interfacing the resulting 3D image reconstructions with canopy-level models of light interception, allowing calculation of intercepted light and radiation use efficiency. The challenge of this project is to develop a parsimonious pipeline in which a lesser amount of pictures is compensated by the use of prior knowledge on plant development, which improves the efficiency of the process. Segmentation of the plant into individual organs is a key process for calculating architectural and growth properties. Because plants have a complex architecture, we combine image analysis routines with a model of plant development that informs the image analysis routine on the expected number and size of organs. This improves efficiency of the process and also reduces the number of images per plant, a necessary condition for high throughput phenotyping. We believe that this method will greatly improve the capability of the Plant Science community for genetic analyses at high throughput of variables hardly accessible in the field such as architecture, response of organ growth to environmental conditions or radiation use efficiency. This will in turn improve our ability for the modeling of genetic variability of plant responses to environmental cues associated for example with climate change.
Michael Mielewczik obtained a Diploma in Biology from Heinrich-Heine-University Düsseldorf, Germany, in 2007. After working as a Scientific Assistant at Forschungszentrum Jülich GmbH, Institute of Chemistry and Dynamics of the Geosphere (Phytosphere), Germany, he worked at ETH Zürich, Institute of Agricultural Sciences, Switzerland on the, “Optimization and application of image based phenotyping approaches with a focus on plant phenotyping and near infrared imaging” and obtained a PhD on this topic. His research interests include (i) high-throughput and high-resolution monitoring by using computer-assisted image-based phenotyping to investigate the influence of environmental effects and metabolism on plant growth and architecture, and (ii) the optimization of image-acquisition, processing and analysis in the framework of image-based phenotyping platforms in biomedical and clinical settings. His AgreenSkills fellowship in Montpellier was dedicated to the high-throughput phenotyping in an European Plant Phenotyping Network 2020 platform. He is currently working at Rothamsted Research as a Systems Modeller. As a member of the TSARA (Towards Sustainable and Resilient Agriculture) project he is investigating means to support the development of pathways to deliver the UN Sustainable Development Goals (SDG) and targets, especially those relevant to agriculture, the bioeconomy and the terrestrial environment.
Pfeifer J, Mielewczik M, Friedli M, Kirchgessner N, Walter A., 2018. Non-destructive measurement of soybean leaf thickness via X-ray computed tomography allows the study of diel leaf growth rhythms in the third dimension. Journal of Plant Research, 131, 111-124.
Pradal C, Artzet S, Chopard J, Dupuis D, Fournier C, Mielewczik M, Nègre V, Neveu P, Parigot D, Valduriez P, Cohen-Boulakia S, 2017. InfraPhenoGrid: A scientific workflow infrastructure for Plant Phenomics on the Grid. Future Generation Computer Systems 67, 341-353.
Dhutia NM, Zolgharni M, Mielewczik M, Negoita M, Sacchi S, Manoharan K, Francis DP, Cole GD, 2017. Open-source, vendor-independent, automated multi-beat tissue Doppler echocardiography analysis. The International journal of Cardiovascular Imaging, 33, 1135-1148.
Negoita M, Zolgharni M, Dadkho E, Pernigo M, Dhutia NM, Mielewczik M, Cole GD, Francis DP., 2016. Frame rate required for speckle tracking echocardiography: A quantitative clinical study with open-source, vendor independent software. International Journal of Cardiology, 218, 31-36.
Mielewczik M, Friedli M, Kirchgessner N, Walter A., 2013. Diel leaf growth of soybean: a novel method to analyze two-dimensional leaf expansion in high temporal resolution based on a marker tracking approach (Martrack Leaf). Plant Methods 9(1), 30. Doi: 1186.1746- 4811-9-30.