Prediction of Biomass Yield in Bioenergy Sorghum Using Unmanned Aerial Systems.

 Prediction of biomass yield in bioenergy sorghum using unmanned aerial systems

N. Ace Pugh, Jinha Jung, David W. Horne, Anjin Chang, Murilo Maeda, Juan Landivar, and William L. Rooney

 

To meet the energy demands of a growing population and a changing climate, society will require alternative and renewable sources of energy. Bioenergy crops, such as sorghum, could help to address this need in the future. However, it is costly to phenotype bioenergy sorghum. Thus, the development of high-throughput techniques, particularly unmanned aerial systems, could allow researchers to more quickly evaluate material and do so at least as efficiently as traditional ground-phenotyping methodologies. In this study, a rotary-wing unmanned aerial system was used to evaluate a diverse population of bioenergy sorghum in Corpus Christi, Texas in 2016. Using both RGB and multispectral sensors, various measurements were obtained including plant height, canopy volume, canopy coverage, and several different vegetation indices. Preliminary results indicate that remote sensing may be an effective tool in predicting biomass in sorghum, with a strong relationship (0.72 R²) being shown between harvested total plot weight and the estimate of canopy volume provided by the unmanned aerial system. Using multiple regression and including several remote-sensing measurements in a predictive model further improves this relationship (0.81 R²).