Growth modeling of soybean plants from different land use intensities using close range RGB image time series
This thesis requires a two month internship beginning in may 2022 to Leibniz Centre for Agricultural Landscape Research (ZALF) in Müncheberg 50km East of Berlin, Germany for two month. Within this that time the ZALF can offer regular german student salary and helps to find or offers accommodation. While in Müncheberg the student will take part of field work and generate weekly photo campaigns as part of the thesis.
We also offer similar projects in this field which differentiate plants or a related research questions.
The student will how to run image analysis research from dataset generation to dateset analysis in agricultural environment. Further the student will learn how to train and evaluate neural networks using state-of-the-art techniques. Also, excellent opportunities to publish your research results in the form of a scientific publication.
- Strong programming skills in one of the following programming languages: R, Python, Java
- Knowledge in image analysis and openCVexperience with machine learning frameworks like PyTorch, KerasInterest on agricultural or environmental science or engineeringInterest in an interdisciplinary Master's thesisExperience in literature research and evaluation
- Pål Halvorsen
- Michael Riegler
- Dr. Kathrin Grahmann - firstname.lastname@example.org
Leibniz Centre for Agricultural Landscape Research (ZALF)