Remote Sensing for Precision Nitrogen Management

© 2022 by the authors; CC BY-NC-ND license

UAS; multiple sensors; vegetation index; leaf nitrogen accumulation; plant nitrogen accumulation; pasture quality; airborne hyperspectral imaging; random forest regression; sun-induced chlorophyll fluorescence (SIF); SIF yield indices; upward; downward; leaf nitrogen concentration (LNC); wheat (Triticum aestivum L.); laser-induced fluorescence; leaf nitrogen concentration; back-propagation neural network; principal component analysis; fluorescence characteristics; leaf nitrogen concentration; canopy nitrogen density; radiative transfer model; hyperspectral; winter wheat; flooded rice; pig slurry; aerial remote sensing; vegetation indices; N recommendation approach; Mediterranean conditions; nitrogen; vertical distribution; plant geometry; remote sensing; maize; UAV; multispectral imagery; LNC; vegetation index; non-parametric regression; radiative transfer model; red-edge; NDRE; dynamic change model; sigmoid curve; grain yield prediction; leaf chlorophyll content; red-edge reflectance; spectral index; winter wheat; precision N fertilization; chlorophyll meter; NDVI; NDRE; NNI; canopy reflectance sensing; N mineralization; farmyard manures; Triticum aestivum; leaf nitrogen concentration; discrete wavelet transform; partial least squares; hyper-spectra; rice; nitrogen management; remote sensing; multispectral imagery; reflectance index; multiple variable linear regression; Lasso model; Multiplex^®3 sensor; nitrogen balance index; nitrogen nutrition index; nitrogen status diagnosis; precision nitrogen management; terrestrial laser scanning; spectrometer; plant height; vegetation indices; biomass; nitrogen concentration; precision agriculture; leaf nitrogen concentration; leaf nitrogen accumulation; unmanned aerial vehicle (UAV); digital camera; vegetation indices; leaf chlorophyll concentration; portable chlorophyll meter; crop; PROSPECT-D; sensitivity analysis; remote sensing; radiative transfer model; UAV multispectral imagery; spectral vegetation indices; machine learning; plant nutrition; canopy spectrum; non-destructive nitrogen status diagnosis; drone; multispectral camera; SPAD; smartphone photography; fixed-wing UAV remote sensing; nitrogen status diagnosis; random forest; precision nitrogen management; machine learning; canopy reflectance; crop N status; Capsicum annuum; proximal optical sensors; nitrogen status diagnosis; Dualex sensor; precision nitrogen management; leaf position; proximal sensing; nitrogen balance index; cross-validation; feature selection; hyperparameter tuning; image processing; image segmentation; nitrogen fertilizer recommendation; supervised regression; RapidSCAN sensor; nitrogen recommendation algorithm; in-season nitrogen management; nitrogen use efficiency; yield potential; yield responsiveness; standard normal variate (SNV); continuous wavelet transform (CWT); wavelet features optimization; competitive adaptive reweighted sampling (CARS); partial least square (PLS); UAV; random forest; nitrogen; maize; grapevine; hyperparameter optimization; machine learning; multispectral imaging; nitrogen; precision viticulture; UAV; RGB; multispectral; coverage adjusted spectral index; vegetation index; vegetation coverage; random frog algorithm; precision nitrogen management; active canopy sensing; integrated sensing system; machine learning; nitrogen nutrition index; discrete NIR spectral band data; soil total nitrogen concentration; moisture absorption correction index; particle size correction index; coupled elimination