Sorghum plant height and yield prediction using multispectral data and sUAS
DOI:
https://doi.org/10.15517/d981kg61Keywords:
remote sensing, breeding, random forest, phenotypingAbstract
Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic breeding, 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, DTMs (digital terrain models), and DSMs (digital surface models). Manual plant height measurements were used for correlation and simple linear regression analysis, while biomass was predicted using random forest regression. Results. 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. DTMs and DSMs derived from multispectral imagery predicted plant height accurately in later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into models enhanced biomass prediction. The findings showed that sUAS-mounted sensors and multispectral indices are promising tools for phenotyping in sorghum breeding programs in Costa Rica.
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