Abstract
A key discipline in plant research is the evaluation of plants’ response to different environmental conditions in order to infer plant functioning. To this end, phenotypes of different genotypes, i.e. different cultivars or mutants and wild type, are compared. In biological research, that is mostly done by growing model plants such as Arabidopsis thaliana, Nicotiana tabacum or Oryza sativa under controlled environmental conditions, followed by the assessment of the phenotypes in order to investigate functional traits. Historically, this has involved manual investigations, which are labor- and time-intensive. In this project, we developed a simple and naïve approach toward automated plant classification by using top view photos of Arabidopsis thaliana. The project was divided into two main parts: (a) Image Analysis with the image-processing package Fiji; (b) Explorative Data Analysis with Python in Jupyter Notebook. Availability: The code for this project is available at https://github.com/rwehler/plant-image-analysis.
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Lamprecht, F., Robinson, C., Wehler, R. (2022). Plant Image Analysis. In: Dörpinghaus, J., Weil, V., Schaaf, S., Apke, A. (eds) Computational Life Sciences. Studies in Big Data, vol 112. Springer, Cham. https://doi.org/10.1007/978-3-031-08411-9_17
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