Image analysis of Sclerotinia disease symptoms in quinoa

This project focuses on the development and testing of an image-based workflow for quantifying Sclerotinia disease symptoms in quinoa under controlled conditions. Quinoa is a genetically diverse crop species, while Sclerotinia sclerotiorum serves as an important fungal pathogen causing stem lesions, tissue necrosis, wilting, and plant collapse. Together, this plant-pathogen system provides a suitable model to study quantitative disease responses and their phenotypic variation. The student will contribute to the standardization and testing of image-based disease phenotyping. This includes the organization of image datasets, and metadata handling, and the comparison of image-derived lesion measurements with manually recorded disease traits such as lesion length and disease severity.

The project will provide hands-on experience in plant phenotyping, digital image analysis, data organization, and basic interpretation of disease-related traits. It is directly linked to ongoing research on quantitative resistance to Sclerotinia in quinoa and will contribute to the improvement of phenotyping workflow of the experiments and downstream genetic analyses.

General learning outcomes

• Understand the biological background of Sclerotinia disease symptoms in quinoa.
• Develop basic skills in interpreting disease symptom progression over time.
• Gain practical experience in plant disease phenotyping under controlled conditions.
• Learn how to organize, annotate, and curate image datasets linked to biological metadata.
• Develop basic skills in image preprocessing and image-based trait extraction.