The project

Late blight caused by Phytophtora infestans is the most important disease of potato. Without control, late blight leads to severe damage to foliage and tubers, which reduce yield. Its control can be achieve by chemical application or breeding resistant varieties. However, traditional methods of phenotyping of leaf blight in potato breeding is time, cost, and labor intensive. Furthermore, spectral data have been demonstrated to be useful in the study of diseases. However, the use of multi- or hyperspectral imaging from unmanned aerial vehicles (UAV) in potato breeding to assess P. infestans infestation has not been largely evaluated so far. Therefore, the focus of this MSc thesis will be to use multi- and hyperspectral imaging to phenotype late blight in segregating potato populations by the implementation of modern machine learning methods.

Research activities and methods
The activities of the current project involve (but are not limited to)

• Phenotypic assessment of leaf blight in field experiment
• UAV data acquisition and processing
• Classification of dataset and model training
• Statistical analyses and accuracy assessment of the method

Qualification and interest
We are looking for candidates with the following qualification and interest:

• Enrolled in a graduate program in (bio)informatics, quantitative biology, agricultural science, or related fields.
• Willingness to learn spectral data acquisition and processing
• Interest to learn machine learning algorithms
• Interest in statistical analyses and interpretation.
• Competent in written and spoken English.
• Motivation and good work ethics.

Our offer

• a research oriented MSc thesis at the interplay between the Leibniz institute of plant genetics and crop plant research (IPK) and the Institute for Breeding Research on Agricultural Crops which is part of the Federal Research Center for Cultivated Plants (JKI).
• an international and multi-disciplinary group with expertise on biostatistics, quantitative genetics, plant genomics, molecular genetics, plant breeding, bioinformatics and plant physiology working on current topics of breeding methodology and crop genetics.
• an enabling working environment and friendly colleagues, state-of-the-art plant cultivation facilities, laboratories and an experimental field.
• Our institute can be reached within 17 minutes by train from Rostock central station.

More information
For further details on project description and application, please contact
Dr. Delphine Van Inghelandt
Email: Delphine.inghelandt@julius-kuehn.de