Remote Sensing and 3D Imagery Driven Phenotyping for Plant Physiological Trait Analysis and Yield Prediction in Winter Wheat 

This research project is aimed at students with an interest in agronomy, plant sciences, and modern remote sensing, and investigates the potential of multispectral sensor data and 3D imagery for the phenotyping plant physiological traits and breeding-relevant characteristics in winter wheat (triticum aestivum). Within the framework of a practice-oriented field experiment, high-resolution remote sensing data and 3D imagery data are collected and combined with classical plant physiological measurements as well as yield parameters. The objective is to identify and quantify relationships between remote sensing parameters, 3D imagery, physiological processes, and yield parameters. 

The project provides fundamental knowledge on the integration of remote sensing and plant physiology and demonstrates in a practical context how digital technologies can contribute to increased efficiency in agriculture and plant breeding. 

General learning outcomes

• Improved understanding of the relationship between remote sensing data and plant physiological traits 

• Improved knowledge of key plant physiological processes and their relevance for crop performance 

• Improved skills for interpreting multispectral indices in relation to crop status, stress and productivity 

• Introduction to statistical analysis methods for agricultural and remote sensing data 

• Practical experience in field data collection and experimental design