Unverified paper record
Mapping plant-scale variation in crop physiological traits and water fluxes
bioRxiv · 25 Feb 2025 · 10.1101/2025.02.21.639464
Abstract
Nitrogen (N) is a vital plant element, affecting plant physiological processes, carbon and water fluxes and ultimately crop yields. However, N uptake by crops can vary over fine spatiotemporal scales, and optimising the application of N-fertiliser to maximise crop performance is challenging. To investigate the potential of spatially mapping the impact of N fertiliser application on crop physiological performance and yield, we leverage both optical and thermal data sampled from drone platforms and ground-level leaf measurements, across a range of different N, Sulphur (S) and sucrose treatments in winter wheat. Using leaf level hyperspectral reflectance data, leaf chlorophyll content was accurately modelled across fertiliser treatments via partial least squares regression (PLSR; R2= 0.93, P < 0.001). Leaf photosynthetic capacity (Vcmax) exhibited a strong linear relationship with leaf chlorophyll (R2 = 0.77; P < 0.001). Using drone-acquired MERIS terrestrial chlorophyll index (MTCI) values as a proxy for leaf chlorophyll (R2 = 0.76; P < 0.001), Vcmax was spatially mapped at the centimetre-scale. Thermal drone and ground measurements demonstrated that N application leads to cooler leaf temperatures, which led to a strong relationship with ground-measured leaf stomatal conductance (R2= 0.6; P < 0.01). Final grain yield was most accurately predicted by optical reflectance (MTCI, R2 = 0.94; P < 0.001). Precise retrieval of leaf-level crop performance indicators from drones establishes significant potential for optimising fertiliser application, to reduce environmental costs and improve yields.
Plant phenotyping relevance
ドローンの光学・熱画像と回帰モデルを用いて、葉クロロフィル、Vcmax、気孔コンダクタンス、収量などの植物形質を空間推定・検証しており、形質取得手法が研究の中心である。
abstractUsing leaf level hyperspectral reflectance data, leaf chlorophyll content was accurately modelled across fertiliser treatments via partial least squares regression
abstractUsing drone-acquired MERIS terrestrial chlorophyll index (MTCI) values as a proxy for leaf chlorophyll
abstractVcmax was spatially mapped at the centimetre-scale
abstractPrecise retrieval of leaf-level crop performance indicators from drones establishes significant potential
Code and data availability
The paper describes plant phenotyping measurements (hyperspectral reflectance, thermal and multispectral drone imagery, PLSR models) but provides no public repository, dataset, code, or supplement URL. The Data availability statement only says to contact the corresponding author, so any paper-specific data/code is only
No evidence-backed public reproduction asset is currently recorded.
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