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Remote Sensing for Precision Nitrogen Management

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 s...

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Bibliographic Details
Main Author:	Miao, Yuxin
Other Authors:	Khosla, Raj, Mulla, David J.
Format:	eBook
Language:	English
Published:	Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:	Machine Learning Leaf Chlorophyll Content Standard Normal Variate (snv) Nni Laser-induced Fluorescence Capsicum Annuum Supervised Regression Yield Potential Plant Nitrogen Accumulation Vegetation Index Crop N Status Hyperspectral N Recommendation Approach Feature Selection Reflectance Index Precision Viticulture Aerial Remote Sensing Canopy Reflectance Sensing Spectral Index Nitrogen Red-edge History Of Engineering & Technology / Bicssc Red-edge Reflectance Sun-induced Chlorophyll Fluorescence (sif) Spectrometer Cross-validation Mediterranean Conditions Spad Technology: General Issues / Bicssc Back-propagation Neural Network Leaf Chlorophyll Concentration Proximal Optical Sensors Wheat (triticum Aestivum L.) Precision N Fertilization Competitive Adaptive Reweighted Sampling (cars) Radiative Transfer Model Prospect-d Biomass Vegetation Coverage Nitrogen Concentration Airborne Hyperspectral Imaging Leaf Nitrogen Accumulation Uav Nitrogen Status Diagnosis Lasso Model Proximal Sensing Canopy Nitrogen Density Rice Rgb Grapevine Maize Leaf Nitrogen Concentration Pig Slurry Flooded Rice Canopy Spectrum Plant Nutrition Soil Total Nitrogen Concentration Particle Size Correction Index Leaf Nitrogen Concentration (lnc) Ndvi Plant Height Coupled Elimination Hyperparameter Optimization Nitrogen Recommendation Algorithm Nitrogen Nutrition Index Lnc Discrete Nir Spectral Band Data Sif Yield Indices Active Canopy Sensing Precision Agriculture Random Forest Digital Camera Multispectral Imaging Image Processing Multispectral Imagery Partial Least Square (pls) Portable Chlorophyll Meter In-season Nitrogen Management Nitrogen Use Efficiency Sigmoid Curve Grain Yield Prediction Moisture Absorption Correction Index Downward Ndre Unmanned Aerial Vehicle (uav) Rapidscan Sensor Non-parametric Regression Spectral Vegetation Indices N Mineralization Partial Least Squares Pasture Quality Wavelet Features Optimization Chlorophyll Meter Multispectral Camera Leaf Position Principal Component Analysis Continuous Wavelet Transform (cwt) Nitrogen Management Triticum Aestivum Multiple Sensors Uas Crop Discrete Wavelet Transform Random Frog Algorithm Vertical Distribution Dualex Sensor Random Forest Regression Uav Multispectral Imagery Smartphone Photography Multiplex®3 Sensor Sensitivity Analysis Vegetation Indices Terrestrial Laser Scanning Remote Sensing Integrated Sensing System Multiple Variable Linear Regression Winter Wheat Fixed-wing Uav Remote Sensing Drone Multispectral Non-destructive Nitrogen Status Diagnosis Dynamic Change Model Fluorescence Characteristics Nitrogen Balance Index Upward Hyper-spectra Environmental Science, Engineering & Technology / Bicssc Precision Nitrogen Management Hyperparameter Tuning Yield Responsiveness Nitrogen Fertilizer Recommendation Canopy Reflectance Farmyard Manures Image Segmentation Coverage Adjusted Spectral Index Plant Geometry
Online Access:	https://www.mdpi.com/books/book/6326 https://directory.doabooks.org/handle/20.500.12854...
Collection:	Directory of Open Access Books - Collection details see MPG.ReNa

Description
Summary:	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.
Item Description:	Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:	1 electronic resource (602 p.)
ISBN:	9783036557106 9783036557090 books978-3-0365-5710-6

Remote Sensing for Precision Nitrogen Management

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