Prediction of sorghum biomass based on image based features derived from time series of UAV images

High throughput plant phenotyping has gained significant interest in the plant science community due to its potential impact in advancing the use of advanced plant genetics for problems ranging from global food security to biomass-based energy crops. While traditional collection of field-based phenotypes is manual, automated remote sensing-based methods can reduce the manual requirements, expand the number of sampled points, and accelerate associations with genotypes. In this preliminary work, we use multiple types of features derived from multi-temporal UAV-based hyperspectral and RGB image data for prediction of sorghum biomass. Considering the nonlinear properties of the spectral input features, multiple layer perception (MLP) neural networks and support vector regression (SVR) are explored for predicting dry biomass. The analysis is conducted on datasets acquired during June-August 2016 over an agricultural test field at the Agronomy Center for Research and Education (ACRE) at Purdue University.