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AI-driven prediction of plant physiological traits in hemp using UAV-based multispectral imagery
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DOI：10.3389/frsen.2026.1843209.png)
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Abstract
En 中文
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.
Keywords:
machine learning
transpiration
photosynthesis
industrial hemp
stomatal conductance
nitrogen management
UAV multispectral imaging
SPAD chlorophyll
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