Trait-based modeling of buffalograss seed yield using UAV-derived plant height and canopy nitrogen concentration

Abstract

Accurate seed-yield prediction is essential for optimizing nitrogen (N) management in buffalograss seed production. However, current UAV-based approaches often rely directly on vegetation indices (VIs), which provide limited physiological insight and not transfer well across growing seasons. To address this limitation, we developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC), representing crop structural and physiological status, respectively. Field experiments were conducted from 2022 to 2024 under seven N application rates. Using data from 2022 and 2023, we calibrated a quadratic PH-CNC model and then evaluated its predictive performance with an independent 2024 dataset. We also compared this framework with a conventional direct VI-based model. The trait-based model explained 89% of the variation in seed yield during calibration and showed better cross-year predictive performance than the VI-based model (R 2 = 0.70, NRMSE = 17% versus R 2 = 0.52, NRMSE = 22%). In addition, the model captured the decline in seed yield under excessive N input, indicating that it reflected biologically meaningful crop responses. These results demonstrated that combining structural and physiological traits can provide a more robust and interpretable alternative to conventional VI-based methods for UAV-based yield prediction. This framework has practical potential for improving precise and sustainable N management in buffalograss seed production.

Code and data availability

The supplied blocks describe UAV imagery acquisition, field trait measurements, and RF modeling for buffalograss seed yield, but contain no data availability statement text, no public dataset deposit, no author code repository, and no trained model release. The only URL present (forestry.gov.cn) is a cited reference, a

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