Mississippi State University
Jenkins, Johnie N.
Perkins, Andy D.
Date of Degree
Graduate Thesis - Open Access
Master of Science (M.S.)
James Worth Bagley College of Engineering
Department of Computer Science and Engineering
Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.
United States Department of Agriculture - Agricultural Research Service
Waldbieser, Joshua Carl, "Phenotyping cotton compactness using machine learning and UAS multispectral imagery" (2023). Theses and Dissertations. 6035.