LEADER 07097nam 2202017z- 450 001 9910637794503321 005 20231214132941.0 010 $a3-0365-5710-5 035 $a(CKB)5470000001631591 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/94502 035 $a(EXLCZ)995470000001631591 100 $a20202212d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRemote Sensing for Precision Nitrogen Management 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (602 p.) 311 $a3-0365-5709-1 330 $aThis 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. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 606 $aEnvironmental science, engineering & technology$2bicssc 610 $aUAS 610 $amultiple sensors 610 $avegetation index 610 $aleaf nitrogen accumulation 610 $aplant nitrogen accumulation 610 $apasture quality 610 $aairborne hyperspectral imaging 610 $arandom forest regression 610 $asun-induced chlorophyll fluorescence (SIF) 610 $aSIF yield indices 610 $aupward 610 $adownward 610 $aleaf nitrogen concentration (LNC) 610 $awheat (Triticum aestivum L.) 610 $alaser-induced fluorescence 610 $aleaf nitrogen concentration 610 $aback-propagation neural network 610 $aprincipal component analysis 610 $afluorescence characteristics 610 $acanopy nitrogen density 610 $aradiative transfer model 610 $ahyperspectral 610 $awinter wheat 610 $aflooded rice 610 $apig slurry 610 $aaerial remote sensing 610 $avegetation indices 610 $aN recommendation approach 610 $aMediterranean conditions 610 $anitrogen 610 $avertical distribution 610 $aplant geometry 610 $aremote sensing 610 $amaize 610 $aUAV 610 $amultispectral imagery 610 $aLNC 610 $anon-parametric regression 610 $ared-edge 610 $aNDRE 610 $adynamic change model 610 $asigmoid curve 610 $agrain yield prediction 610 $aleaf chlorophyll content 610 $ared-edge reflectance 610 $aspectral index 610 $aprecision N fertilization 610 $achlorophyll meter 610 $aNDVI 610 $aNNI 610 $acanopy reflectance sensing 610 $aN mineralization 610 $afarmyard manures 610 $aTriticum aestivum 610 $adiscrete wavelet transform 610 $apartial least squares 610 $ahyper-spectra 610 $arice 610 $anitrogen management 610 $areflectance index 610 $amultiple variable linear regression 610 $aLasso model 610 $aMultiplex®3 sensor 610 $anitrogen balance index 610 $anitrogen nutrition index 610 $anitrogen status diagnosis 610 $aprecision nitrogen management 610 $aterrestrial laser scanning 610 $aspectrometer 610 $aplant height 610 $abiomass 610 $anitrogen concentration 610 $aprecision agriculture 610 $aunmanned aerial vehicle (UAV) 610 $adigital camera 610 $aleaf chlorophyll concentration 610 $aportable chlorophyll meter 610 $acrop 610 $aPROSPECT-D 610 $asensitivity analysis 610 $aUAV multispectral imagery 610 $aspectral vegetation indices 610 $amachine learning 610 $aplant nutrition 610 $acanopy spectrum 610 $anon-destructive nitrogen status diagnosis 610 $adrone 610 $amultispectral camera 610 $aSPAD 610 $asmartphone photography 610 $afixed-wing UAV remote sensing 610 $arandom forest 610 $acanopy reflectance 610 $acrop N status 610 $aCapsicum annuum 610 $aproximal optical sensors 610 $aDualex sensor 610 $aleaf position 610 $aproximal sensing 610 $across-validation 610 $afeature selection 610 $ahyperparameter tuning 610 $aimage processing 610 $aimage segmentation 610 $anitrogen fertilizer recommendation 610 $asupervised regression 610 $aRapidSCAN sensor 610 $anitrogen recommendation algorithm 610 $ain-season nitrogen management 610 $anitrogen use efficiency 610 $ayield potential 610 $ayield responsiveness 610 $astandard normal variate (SNV) 610 $acontinuous wavelet transform (CWT) 610 $awavelet features optimization 610 $acompetitive adaptive reweighted sampling (CARS) 610 $apartial least square (PLS) 610 $agrapevine 610 $ahyperparameter optimization 610 $amultispectral imaging 610 $aprecision viticulture 610 $aRGB 610 $amultispectral 610 $acoverage adjusted spectral index 610 $avegetation coverage 610 $arandom frog algorithm 610 $aactive canopy sensing 610 $aintegrated sensing system 610 $adiscrete NIR spectral band data 610 $asoil total nitrogen concentration 610 $amoisture absorption correction index 610 $aparticle size correction index 610 $acoupled elimination 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 615 7$aEnvironmental science, engineering & technology 700 $aMiao$b Yuxin$4edt$01328596 702 $aKhosla$b Raj$4edt 702 $aMulla$b David J$4edt 702 $aMiao$b Yuxin$4oth 702 $aKhosla$b Raj$4oth 702 $aMulla$b David J$4oth 906 $aBOOK 912 $a9910637794503321 996 $aRemote Sensing for Precision Nitrogen Management$93038699 997 $aUNINA