IntroductionIndustrial hemp (Cannabis sativa L.) is a multipurpose bio‐economy crop capable of producing fiber, grain, and biomass while contributing to soil health and carbon sequestration. Realizing this potential requires optimized nitrogen (N) management, as N strongly regulates plant growth, yield, and fiber quality, while inefficient or excessive N use can cause environmental harm. Conventional N diagnostics based on destructive sampling are labor‐intensive and lack the spatial resolution needed for precision management. Uncrewed aerial vehicle (UAV) multispectral imaging offers a high-throughput, non‐destructive alternative; however, hemp remains underrepresented in UAV‐based N studies, particularly in linking spectral data to physiological traits associated with N metabolism.MethodsTo address this gap, two field experiments were conducted at the North Carolina A&T State University research farm using dual‐purpose and fiber‐type hemp cultivars under contrasting N regimes during the 2024 and 2025 growing seasons. Multispectral imagery was collected using a WingtraOne GEN II UAV equipped with a MicaSense RedEdge‐P camera. From reflectance mosaics, 33 vegetation indices (VIs) were computed, and the top seven were selected using Spearman's correlation analysis. Ground measurements included SPAD chlorophyll readings and gas‐exchange traits, i.e., net photosynthetic rate (Pn), stomatal conductance (Gs), and transpiration rate (E), using a LI‐COR 6800 system. Using SAS Viya, multiple supervised learning models were developed to predict SPAD, Pn, Gs, and E from UAV‐derived VIs.ResultsRed‐edge and green‐based indices showed very strong correlations with SPAD (ρ = 0.9078−0.9375), while NDWI showed a strong negative relationship (ρ = −0.9623). For Pn, GNDVI and CIG were strongly correlated (ρ ≈ 0.89), with NDWI negatively associated (ρ = −0.8934). Gs and E exhibited moderate correlations (ρ ≈ 0.73−0.84 and 0.75−0.80). Linear regression achieved R2 = 0.88 for SPAD, while a generalized additive model predicted Pn with R2 = 0.87. Quantile regression performed best for Gs and E (R2 = 0.82 and 0.75; Gs: ASE ≈0.013, MAE ≈0.086; E: ASE ≈0.0003, MAE ≈0.014).DiscussionThese results demonstrate a scalable framework for UAV‐based phenotyping to support precision N management in hemp.