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

Abstract

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.

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

“…However, we recognize many other species, from herbaceous annual crops (Grenzdörffer, 2019) all the way up to perennial trees (López‐Granados et al., 2019), are using UAS tools to enhance their small‐plot research and breeding. Forage grasses and silage provide an especially interesting case to look at breeding for the aboveground biomass throughout the season (Alvarez‐Mendoza et al., 2022; de Oliveira et al., 2021; Nakasagga et al., 2022), while cassava ( Manihot esculenta Crantz), peanut ( Arachis hypogaea ), and potato ( Solanum tuberosum L.) UAS research (de Jesus Colwell et al., 2021; Sarkar et al., 2021; Selvaraj et al., 2020) has shown that below ground biomass yield can be estimated from UAS. Each of these species had to develop a unique methodology.…”