Machine learning-based prediction of sorghum biomass from UAV multispectral imagery data

Unmanned aerial vehicle (UAV)-based remote sensing applications in plant phenotyping have received attention in modern plant breeding programs that increasingly have the need to automate time-consuming manual measurements of agronomic traits. This paper focuses on the prediction of sorghum biomass using machine learning algorithms such as Linear Regression, KNeighbors Regressor, and the XGBoost Regressor. Results from a field experiment of 344 sorghum genotypes conducted at the Donald Danforth Plant Science Center (Saint Louis, MO, USA) showed accurate prediction models. The K-Neighbors Regression model performed better than the other two models (R ² =0.65, RMSE =4968.60kg/ha). The developed approach in this study could be used as a decision support tool for sorghum biomass phenotyping in breeding programs.