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dc.contributor.editorMiao, Yuxin
dc.contributor.editorKhosla, Raj
dc.contributor.editorMulla, David J.
dc.date.accessioned2022-12-06T16:08:37Z
dc.date.available2022-12-06T16:08:37Z
dc.date.issued2022
dc.identifierONIX_20221206_9783036557090_25
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/94502
dc.description.abstractThis 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.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TQ Environmental science, engineering & technology
dc.subject.otherUAS
dc.subject.othermultiple sensors
dc.subject.othervegetation index
dc.subject.otherleaf nitrogen accumulation
dc.subject.otherplant nitrogen accumulation
dc.subject.otherpasture quality
dc.subject.otherairborne hyperspectral imaging
dc.subject.otherrandom forest regression
dc.subject.othersun-induced chlorophyll fluorescence (SIF)
dc.subject.otherSIF yield indices
dc.subject.otherupward
dc.subject.otherdownward
dc.subject.otherleaf nitrogen concentration (LNC)
dc.subject.otherwheat (Triticum aestivum L.)
dc.subject.otherlaser-induced fluorescence
dc.subject.otherleaf nitrogen concentration
dc.subject.otherback-propagation neural network
dc.subject.otherprincipal component analysis
dc.subject.otherfluorescence characteristics
dc.subject.othercanopy nitrogen density
dc.subject.otherradiative transfer model
dc.subject.otherhyperspectral
dc.subject.otherwinter wheat
dc.subject.otherflooded rice
dc.subject.otherpig slurry
dc.subject.otheraerial remote sensing
dc.subject.othervegetation indices
dc.subject.otherN recommendation approach
dc.subject.otherMediterranean conditions
dc.subject.othernitrogen
dc.subject.othervertical distribution
dc.subject.otherplant geometry
dc.subject.otherremote sensing
dc.subject.othermaize
dc.subject.otherUAV
dc.subject.othermultispectral imagery
dc.subject.otherLNC
dc.subject.othernon-parametric regression
dc.subject.otherred-edge
dc.subject.otherNDRE
dc.subject.otherdynamic change model
dc.subject.othersigmoid curve
dc.subject.othergrain yield prediction
dc.subject.otherleaf chlorophyll content
dc.subject.otherred-edge reflectance
dc.subject.otherspectral index
dc.subject.otherprecision N fertilization
dc.subject.otherchlorophyll meter
dc.subject.otherNDVI
dc.subject.otherNNI
dc.subject.othercanopy reflectance sensing
dc.subject.otherN mineralization
dc.subject.otherfarmyard manures
dc.subject.otherTriticum aestivum
dc.subject.otherdiscrete wavelet transform
dc.subject.otherpartial least squares
dc.subject.otherhyper-spectra
dc.subject.otherrice
dc.subject.othernitrogen management
dc.subject.otherreflectance index
dc.subject.othermultiple variable linear regression
dc.subject.otherLasso model
dc.subject.otherMultiplex®3 sensor
dc.subject.othernitrogen balance index
dc.subject.othernitrogen nutrition index
dc.subject.othernitrogen status diagnosis
dc.subject.otherprecision nitrogen management
dc.subject.otherterrestrial laser scanning
dc.subject.otherspectrometer
dc.subject.otherplant height
dc.subject.otherbiomass
dc.subject.othernitrogen concentration
dc.subject.otherprecision agriculture
dc.subject.otherunmanned aerial vehicle (UAV)
dc.subject.otherdigital camera
dc.subject.otherleaf chlorophyll concentration
dc.subject.otherportable chlorophyll meter
dc.subject.othercrop
dc.subject.otherPROSPECT-D
dc.subject.othersensitivity analysis
dc.subject.otherUAV multispectral imagery
dc.subject.otherspectral vegetation indices
dc.subject.othermachine learning
dc.subject.otherplant nutrition
dc.subject.othercanopy spectrum
dc.subject.othernon-destructive nitrogen status diagnosis
dc.subject.otherdrone
dc.subject.othermultispectral camera
dc.subject.otherSPAD
dc.subject.othersmartphone photography
dc.subject.otherfixed-wing UAV remote sensing
dc.subject.otherrandom forest
dc.subject.othercanopy reflectance
dc.subject.othercrop N status
dc.subject.otherCapsicum annuum
dc.subject.otherproximal optical sensors
dc.subject.otherDualex sensor
dc.subject.otherleaf position
dc.subject.otherproximal sensing
dc.subject.othercross-validation
dc.subject.otherfeature selection
dc.subject.otherhyperparameter tuning
dc.subject.otherimage processing
dc.subject.otherimage segmentation
dc.subject.othernitrogen fertilizer recommendation
dc.subject.othersupervised regression
dc.subject.otherRapidSCAN sensor
dc.subject.othernitrogen recommendation algorithm
dc.subject.otherin-season nitrogen management
dc.subject.othernitrogen use efficiency
dc.subject.otheryield potential
dc.subject.otheryield responsiveness
dc.subject.otherstandard normal variate (SNV)
dc.subject.othercontinuous wavelet transform (CWT)
dc.subject.otherwavelet features optimization
dc.subject.othercompetitive adaptive reweighted sampling (CARS)
dc.subject.otherpartial least square (PLS)
dc.subject.othergrapevine
dc.subject.otherhyperparameter optimization
dc.subject.othermultispectral imaging
dc.subject.otherprecision viticulture
dc.subject.otherRGB
dc.subject.othermultispectral
dc.subject.othercoverage adjusted spectral index
dc.subject.othervegetation coverage
dc.subject.otherrandom frog algorithm
dc.subject.otheractive canopy sensing
dc.subject.otherintegrated sensing system
dc.subject.otherdiscrete NIR spectral band data
dc.subject.othersoil total nitrogen concentration
dc.subject.othermoisture absorption correction index
dc.subject.otherparticle size correction index
dc.subject.othercoupled elimination
dc.titleRemote Sensing for Precision Nitrogen Management
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-5710-6
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036557090
oapen.relation.isbn9783036557106
oapen.pages602
oapen.place.publicationBasel


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