Sorghum plant height and yield prediction using multispectral data and sUAS

Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic improvement, essential to meet this demand, relies on advanced technologies to accelerate field phenotyping processes. Objective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights. Materials and methods. Six sorghum genotypes were evaluated in Cañas, Guanacaste, Costa Rica, using a completely randomized design with eight replications per genotype. Multispectral sensor flights were conducted at selected phenological stages to generate vegetation indices, digital terrain models (DTMs), and digital surface models (DSMs). Manual plant height measurements were used for correlation and simple linear regression analyses, while biomass was predicted using random forest regression. Results. The DTMs and DSMs enabled reliable estimation of plant height during early growth stage (R² = 0.53) and achieved higher accuracy at later stages (R²= 0.76; RMSE = 0.13 m). Biomass prediction was most accurate at the booting stage (r= 0.72; RMSE = 1.40 t·ha-¹), with NDRE (Normalized Difference Red-Edge Index) and IKAW (Kawashima Index) identified as the most relevant spectral indices. Conclusions. The DTMs and DSMs derived from multispectral imagery accurately predicted plant height during later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into predictive models enhanced biomass yield prediction. The findings demonstrate that sUAS-mounted sensors and multispectral indices are valuable tools for phenotyping in sorghum breeding programs in Costa Rica.