Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

Efficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages occur concurrently in diverse genotypes. Additionally, vegetation indices (VIs) saturate at high canopy coverage, and vertical growth profiles are difficult to capture using VIs. A novel approach was implemented involving a fusion of complementary spectral and structural information, to calculate intermediate metrics such as crop height model (CHM), crop coverage (CC) and crop volume (CV), which were finally used to calculate dry (DW) and fresh (FW) weight of above-ground biomass in wheat. The intermediate metrics, CHM (R-2 = 0.81, SEE = 4.19 cm) and CC (OA = 99.2%, Kappa = 0.98) were found to be accurate against equivalent ground truth measurements. The metrics CV and CVxVIs were used to develop an effective and accurate linear regression model relationship with DW (R-2 = 0.96 and SEE = 69.2 g/m(2)) and FW (R-2 = 0.89 and SEE = 333.54 g/m(2)). The implemented approach outperformed commonly used VIs for estimation of biomass at all growth stages in wheat. The achieved results strongly support the applicability of the proposed approach for high-throughput phenotyping of germplasm in wheat and other crop species.