Prediction of regrowth and biomass of perennial sorghum using unoccupied aerial systems

Perennial grain sorghum [Sorghum bicolor (L.) Moench] has potential to produce grain and forage while improving soil health, ecosystem services, and carbon soil sequestration but requires further genetic improvement. Unoccupied aerial systems (UAS, also known as drones and unmanned aerial systems) provide opportunities to quickly evaluate plant traits on a large scale with precision. Unoccupied aerial system flights were used to evaluate biomass yield and rhizome characteristics of 100 diverse sorghum hybrids, most being from an interspecific hybridization program, in the establishment year and first year of regrowth. Twenty-one vegetation indices (VIs) with canopy height measurements (CHMs) were processed from seven UAS flights made temporally during each growing season. Regression of the temporal data (VI and CHM) and phenotypic traits, including rhizome characteristics based on plant stand count (PSC), rhizome-derived shoots (RDS), and fresh and dry biomass yields, showed useful predictions when combining temporal VI with CHM and machine learning. Blue chromatic coordinate index (BCC) best predicted all measured traits. If predictions could be generalized, UAS would reduce field evaluation time for perennial sorghum or breeding perennial grasses in general and allow breeders to evaluate additional genotypes. In this study, we found that optimizing flights to specific dates after planting could minimize resource requirements and costs in prediction of regrowth and biomass yield of perennial sorghum.

Automated summary

This study used unmanned aerial systems to evaluate biomass yield and rhizome characteristics of perennial grain sorghum and found that the blue chromatic coordinate index (BCC) best predicted the measured traits. The study also showed that optimizing flights to specific dates after planting could minimize resource requirements and costs.