Short project description

UAV-based remote sensing is a powerful tool for field phenotyping that enables non-destructive measurement of crop physiological traits. High spatial and temporal crop sensing data will facilitate understanding of crop physiology, which enable efficient breeding programs and better-informed decision making. UAV-based RGB or multispectral imagery will be used to train a computer vision-based deep learning model to predict crop physiological traits such as biomass, nitrogen content, and nitrogen nutrition index. This project aims not only to enhance the model prediction accuracy but also to evaluate the robustness and scalability of the trained model. The student will gain experience in methodology of data preprocessing of UAV imagery, programming in Python for implementing deep learning. The student will also have a chance to collaborate with PhD students to conduct on-going field experiment projects.