Sorghum Biomass Prediction Using Uav-Based Remote Sensing Data and Crop Model Simulation

Accurate phenotyping with unmanned aerial vehicles is a remote sensing application that has received recent attention as plant breeders seek to automate the expensive and time consuming traditional manual acquisition of measurements of plant traits. This paper focuses on the prediction of sorghum biomass utilizing high temporal and spatial resolution remote sensing data. Two methods are investigated for biomass prediction. The first uses nonlinear regression models to predict biomass directly from remote sensing data, based on features from Light Detection And Ranging (LiDAR) point clouds and hyperspectral data. The second strategy focuses on the biophysical sorghum crop model, APSIM, first, using remote sensing data to parametrize the crop model, and then simulating the biomass. Results from both approaches are provided and evaluated for an agricultural test field at the Agronomy Center for Research and Education (ACRE) at Purdue University.