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Remote Sensing for Precision Nitrogen Management
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This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment.
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Keywords
- active canopy sensing
- aerial remote sensing
- airborne hyperspectral imaging
- back-propagation neural network
- Biomass
- canopy nitrogen density
- canopy reflectance
- canopy reflectance sensing
- canopy spectrum
- Capsicum annuum
- chlorophyll meter
- competitive adaptive reweighted sampling (CARS)
- continuous wavelet transform (CWT)
- coupled elimination
- coverage adjusted spectral index
- crop
- crop N status
- cross-validation
- digital camera
- discrete NIR spectral band data
- discrete wavelet transform
- downward
- drone
- Dualex sensor
- dynamic change model
- Environmental science, engineering & technology
- farmyard manures
- feature selection
- fixed-wing UAV remote sensing
- flooded rice
- fluorescence characteristics
- grain yield prediction
- grapevine
- History of engineering & technology
- hyper-spectra
- hyperparameter optimization
- hyperparameter tuning
- hyperspectral
- Image processing
- image segmentation
- in-season nitrogen management
- integrated sensing system
- laser-induced fluorescence
- Lasso model
- leaf chlorophyll concentration
- leaf chlorophyll content
- leaf nitrogen accumulation
- leaf nitrogen concentration
- leaf nitrogen concentration (LNC)
- leaf position
- LNC
- Machine learning
- Maize
- Mediterranean conditions
- moisture absorption correction index
- multiple sensors
- multiple variable linear regression
- Multiplex®3 sensor
- multispectral
- multispectral camera
- multispectral imagery
- Multispectral Imaging
- N mineralization
- N recommendation approach
- NDRE
- NDVI
- Nitrogen
- nitrogen balance index
- nitrogen concentration
- nitrogen fertilizer recommendation
- nitrogen management
- nitrogen nutrition index
- nitrogen recommendation algorithm
- nitrogen status diagnosis
- nitrogen use efficiency
- NNI
- non-destructive nitrogen status diagnosis
- non-parametric regression
- partial least square (PLS)
- partial least squares
- particle size correction index
- pasture quality
- pig slurry
- plant geometry
- plant height
- plant nitrogen accumulation
- Plant Nutrition
- portable chlorophyll meter
- precision agriculture
- precision N fertilization
- precision nitrogen management
- precision viticulture
- principal component analysis
- PROSPECT-D
- proximal optical sensors
- proximal sensing
- radiative transfer model
- random forest
- random forest regression
- random frog algorithm
- RapidSCAN sensor
- red-edge
- red-edge reflectance
- reflectance index
- Remote sensing
- RGB
- Rice
- sensitivity analysis
- SIF yield indices
- sigmoid curve
- smartphone photography
- soil total nitrogen concentration
- SPAD
- spectral index
- spectral vegetation indices
- spectrometer
- standard normal variate (SNV)
- sun-induced chlorophyll fluorescence (SIF)
- supervised regression
- Technology, engineering, agriculture
- Technology: general issues
- terrestrial laser scanning
- Triticum aestivum
- UAS
- UAV
- UAV multispectral imagery
- unmanned aerial vehicle (UAV)
- upward
- vegetation coverage
- vegetation index
- vegetation indices
- vertical distribution
- wavelet features optimization
- wheat (Triticum aestivum L.)
- winter wheat
- yield potential
- yield responsiveness
Links
DOI: 10.3390/books978-3-0365-5710-6Editions