Signals in the Soil: Subsurface Sensing

In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges.

[2]  G. Taylor,et al.  Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization , 2002 .

[3]  D. J. Brus,et al.  Sampling for Natural Resource Monitoring , 2006 .

[4]  C. Reece Evaluation of a Line Heat Dissipation Sensor for Measuring Soil Matric Potential , 1996 .

[5]  Qiang Cao,et al.  Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice , 2014 .

[6]  N. Breda Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[7]  F. Baret,et al.  Green area index from an unmanned aerial system over wheat and rapeseed crops , 2014 .

[8]  Reza Ehsani,et al.  A Laser Scanner Based Measurement System for Quantification of Citrus Tree Geometric Characteristics , 2009 .

[9]  WhiteheadKen,et al.  Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges1 , 2014 .

[10]  Viacheslav I. Adamchuk,et al.  Review: Sensor systems for measuring soil compaction: Review and analysis , 2008 .

[11]  P. Ruelle,et al.  Comparison of three calibration procedures for TDR soil moisture sensors , 2003 .

[12]  Mehmet C. Vuran,et al.  Time-domain and Frequency-domain Reflectometry Type Soil Moisture Sensor Performance and Soil Temperature Effects in Fine- and Coarse-textured Soils , 2019, Applied Engineering in Agriculture.

[13]  Volkan Isler,et al.  Large Scale Image Mosaic Construction for Agricultural Applications , 2016, IEEE Robotics and Automation Letters.

[14]  Yukio Kosugi,et al.  Characterization of Rice Paddies by a UAV-Mounted Miniature Hyperspectral Sensor System , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Yubin Lan,et al.  An Airborne Multispectral Imaging System Based on Two Consumer-Grade Cameras for Agricultural Remote Sensing , 2014, Remote. Sens..

[16]  Jeffrey W. White,et al.  Active Optical Sensors in Irrigated Durum Wheat: Nitrogen and Water Effects , 2017 .

[17]  Abdul Salam Internet of Things for Sustainable Forestry , 2019 .

[18]  Stefano Giorgi,et al.  Development of a Rapid Soil Water Content Detection Technique Using Active Infrared Thermal Methods for In-Field Applications , 2011, Sensors.

[19]  John P. Fulton,et al.  An overview of current and potential applications of thermal remote sensing in precision agriculture , 2017, Comput. Electron. Agric..

[20]  E. Small,et al.  An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil , 2016, GPS Solutions.

[21]  James H. Everitt,et al.  Airborne videography : current status and future perspectives , 1992 .

[22]  P. Zarco-Tejada,et al.  Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle , 2014, Precision Agriculture.

[23]  Mehmet C. Vuran,et al.  A Theoretical Model of Underground Dipole Antennas for Communications in Internet of Underground Things , 2019, IEEE Transactions on Antennas and Propagation.

[24]  James S. Schepers,et al.  Derivation of a Variable Rate Nitrogen Application Model for In‐Season Fertilization of Corn , 2010 .

[25]  James H. Everitt,et al.  A three-camera multispectral digital video imaging system , 1995 .

[26]  Martin Weis,et al.  Improving the determination of plant characteristics by fusion of four different sensors , 2013 .

[27]  Y. Kerr,et al.  State of the Art in Large-Scale Soil Moisture Monitoring , 2013 .

[28]  Lav R. Khot,et al.  Efficacy of unmanned helicopter in rainwater removal from cherry canopies , 2016, Comput. Electron. Agric..

[29]  S. Jones,et al.  Ground, Proximal, and Satellite Remote Sensing of Soil Moisture , 2019, Reviews of Geophysics.

[30]  Ian F. Akyildiz,et al.  Novel MI-based (FracBot) sensor hardware design for monitoring hydraulic fractures and oil reservoirs , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[31]  Nicolas Tremblay,et al.  Strategies to Make Use of Plant Sensors-Based Diagnostic Information for Nitrogen Recommendations , 2009 .

[32]  F. J. Ponzoni,et al.  Sun and view angle effects on NDVI determination of land cover types in the Brazilian Amazon region with hyperspectral data , 2004 .

[33]  Dante Fratta,et al.  A Survey of Elastic and Electromagnetic Properties of Near-Surface Soils , 2009 .

[34]  Comparison of Twelve Dielectric Moisture Probes for Soil Water Measurement under Saline Conditions , 2008 .

[35]  Jean L. Steiner,et al.  PRECISION OF NEUTRON SCATTERING AND CAPACITANCE TYPE SOIL WATER CONTENT GAUGES FROM FIELD CALIBRATION , 1995 .

[36]  R A Diaz-Varela,et al.  Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. , 2014, Journal of environmental management.

[37]  G. Richard,et al.  Electrical resistivity survey in soil science: a review . , 2005 .

[38]  Mehmet C. Vuran,et al.  Internet of underground things: Sensing and communications on the field for precision agriculture , 2018, 2018 IEEE 4th World Forum on Internet of Things (WF-IoT).

[39]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[40]  Seishi Ninomiya,et al.  Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. , 2016, Functional plant biology : FPB.

[41]  Martin J. Wooster,et al.  High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing , 2016, Remote. Sens..

[42]  Bisun Datt,et al.  A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests using Eucalyptus Leaves , 1999 .

[43]  K. Sudduth,et al.  Soil macronutrient sensing for precision agriculture. , 2009, Journal of environmental monitoring : JEM.

[44]  Michael Pflanz,et al.  Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery , 2016, Remote. Sens..

[45]  Chenghai Yang,et al.  A high-resolution airborne four-camera imaging system for agricultural remote sensing , 2012 .

[46]  S. R. Evett,et al.  Advances in Soil Water Content Sensing: The Continuing Maturation of Technology and Theory , 2005 .

[47]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[48]  Nicolas Tremblay,et al.  A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application , 2009, Precision Agriculture.

[49]  E. Fereres,et al.  Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard , 2013, Precision Agriculture.

[50]  H. Ramon,et al.  Foliar Disease Detection in the Field Using Optical Sensor Fusion , 2004 .

[51]  J. Markwell,et al.  Calibration of the Minolta SPAD-502 leaf chlorophyll meter , 2004, Photosynthesis Research.

[52]  Abdul Salam,et al.  Impacts of Soil Type and Moisture on the Capacity of Multi-Carrier Modulation in Internet of Underground Things , 2016, 2016 25th International Conference on Computer Communication and Networks (ICCCN).

[53]  Azmi Yahya,et al.  A Tractor-mounted, Automated Soil Penetrometer–shearometer Unit for Mapping Soil Mechanical Properties , 2005 .

[54]  Jorge Delgado,et al.  Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques , 2016, Remote. Sens..

[55]  J. Kovacs,et al.  Applications of Low Altitude Remote Sensing in Agriculture upon Farmers' Requests– A Case Study in Northeastern Ontario, Canada , 2014, PloS one.

[56]  Abdul Salam,et al.  Internet of Things for Sustainable Community Development: Introduction and Overview , 2019, Internet of Things.

[57]  David Beamish,et al.  Fundamentals of the capacitive resistivity technique , 2006 .

[58]  Mehmet C. Vuran,et al.  Vehicle-to-barrier communication during real-world vehicle crash tests , 2018, Comput. Commun..

[59]  D. S. Chanasyk,et al.  Field measurement of soil moisture using neutron probes , 1996 .

[60]  G. Campbell,et al.  Probe for Measuring Soil Specific Heat Using A Heat-Pulse Method , 1991 .

[61]  Abdul Salam,et al.  An Underground Radio Wave Propagation Prediction Model for Digital Agriculture , 2019, Inf..

[62]  P. C. Robert,et al.  Using an automated penetrometer and soil EC probe to characterize the rooting zone. , 2000 .

[63]  William R DeTar,et al.  Detection of Soil Properties with Airborne Hyperspectral Measurements of Bare Fields , 2008 .

[64]  Chenghai Yang,et al.  Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability , 2004, Precision Agriculture.

[65]  Li He,et al.  Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices , 2016, Precision Agriculture.

[66]  Francisca López-Granados,et al.  Weed detection for site-specific weed management: mapping and real-time approaches , 2011 .

[67]  Kenneth A. Sudduth,et al.  Comparison of electromagnetic induction and direct sensing of soil electrical conductivity , 2003 .

[68]  Antonio Barrientos,et al.  Mini-UAV Based Sensory System for Measuring Environmental Variables in Greenhouses , 2015, Sensors.

[69]  Reza Ehsani,et al.  Detection of Huanglongbing Disease in Citrus Using Fluorescence Spectroscopy , 2012 .

[70]  Craig S. T. Daughtry,et al.  Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring , 2010, Remote. Sens..

[71]  David W. Lamb,et al.  Radiometry of Proximal Active Optical Sensors (AOS) for Agricultural Sensing , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[72]  Ying Gao,et al.  The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[73]  Neil McKenzie,et al.  Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time , 2011 .

[74]  Andreas Burkart,et al.  Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance , 2015 .

[75]  Abdul Salam Design of Subsurface Phased Array Antennas for Digital Agriculture Applications , 2019, 2019 IEEE International Symposium on Phased Array System & Technology (PAST).

[76]  Mingquan Wu,et al.  Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping , 2017, Remote. Sens..

[77]  D. King Airborne Multispectral Digital Camera and Video Sensors: A Critical Review of System Designs and Applications , 1995 .

[78]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[79]  J. Ruehlmann,et al.  Resistivity mapping with GEOPHILUS ELECTRICUS — Information about lateral and vertical soil heterogeneity , 2013 .

[80]  M. A. Moreno,et al.  Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing , 2014, Precision Agriculture.

[81]  Robert S. Freeland,et al.  Media framing the reception of unmanned aerial vehicles in the United States of America , 2016 .

[82]  Mark W. Smith,et al.  Structure from motion photogrammetry in physical geography , 2016 .

[83]  A. Thomsen,et al.  Mobile TDR for geo-referenced measurement of soil water content and electrical conductivity , 2007, Precision Agriculture.

[84]  Dan S. Long,et al.  On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat , 2015, Precision Agriculture.

[85]  Sylvie M. Brouder,et al.  Chlorophyll meter readings can predict nitrogen need and yield response of corn in the north-central USA , 2006 .

[86]  Nithya Rajan,et al.  Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research , 2016, PloS one.

[87]  S. Labbé,et al.  Getting simultaneous red and near-infrared band data from a single digital camera for plant monitoring applications: theoretical and practical study , 2014 .

[88]  Mehmet C. Vuran,et al.  EM-Based Wireless Underground Sensor Networks , 2018 .

[89]  Stefano Amaducci,et al.  Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery , 2014, Remote. Sens..

[90]  Abdul Salam Internet of Things in Agricultural Innovation and Security , 2019, Internet of Things.

[91]  Mariette Vreugdenhil,et al.  Using Cosmic-Ray Neutron Probes to Monitor Landscape Scale Soil Water Content in Mixed Land Use Agricultural Systems , 2016 .

[92]  A. Thomsen,et al.  Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat , 2006, Precision Agriculture.

[93]  Johanna Link,et al.  Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System , 2014, Remote. Sens..

[94]  Jan Vanderborght,et al.  On the spatio-temporal dynamics of soil moisture at the field scale , 2014 .

[95]  F. López-Granados,et al.  Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images , 2013, PloS one.

[96]  Wulf Amelung,et al.  Proximal gamma-ray spectrometry for site-independent in situ prediction of soil texture on ten heterogeneous fields in Germany using support vector machines , 2017 .

[97]  Abdul Salam Internet of Things for Sustainability: Perspectives in Privacy, Cybersecurity, and Future Trends , 2020 .

[98]  Pedro Antonio Gutiérrez,et al.  A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method , 2015, Appl. Soft Comput..

[99]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[100]  Mehmet C. Vuran,et al.  Di-Sense: In situ real-time permittivity estimation and soil moisture sensing using wireless underground communications , 2019, Comput. Networks.

[101]  J. S. Schepers,et al.  Use of a Chlorophyll Meter to Monitor Nitrogen Status and Schedule Fertigation for Corn , 1995 .

[102]  Abdul Salam,et al.  Internet of Things in Water Management and Treatment , 2019 .

[103]  Christos Argyropoulos,et al.  Soft Microreactors for the Deposition of Conductive Metallic Traces on Planar, Embossed, and Curved Surfaces , 2018, Advanced Functional Materials.

[104]  Jorge Torres-Sánchez,et al.  High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology , 2015, PloS one.

[105]  Abdul Salam,et al.  Underground Environment Aware MIMO Design Using Transmit and Receive Beamforming in Internet of Underground Things , 2019, ICIOT.

[106]  J. Flexas,et al.  UAVs challenge to assess water stress for sustainable agriculture , 2015 .

[107]  Michel Dabas,et al.  Comparison of instruments for geoelectrical soil mapping at the field scale , 2009 .

[108]  F. Egmond,et al.  Gamma Ray Sensor for Topsoil Mapping: The Mole , 2010 .

[109]  Abdul Salam,et al.  Wireless Underground Communications in Sewer and Stormwater Overflow Monitoring: Radio Waves through Soil and Asphalt Medium , 2020, Inf..

[110]  A. Giebel,et al.  Evaluation of the soil penetration resistance along a transect to determine the loosening depth , 2006, Precision Agriculture.

[111]  Chenghai Yang,et al.  Low-cost single-camera imaging system for aerial applicators , 2015 .

[112]  A. Salam,et al.  Internet of Things for Environmental Sustainability and Climate Change , 2019 .

[113]  Andrew E. Suyker,et al.  An alternative method using digital cameras for continuous monitoring of crop status , 2012 .

[114]  D. Siqueira,et al.  MAGNETIC SUSCEPTIBILITY AS INDICATOR OF SOIL QUALITY IN SUGARCANE FIELDS , 2017 .

[115]  Matthew Bardeen,et al.  Selecting Canopy Zones and Thresholding Approaches to Assess Grapevine Water Status by Using Aerial and Ground-Based Thermal Imaging , 2016, Remote. Sens..

[116]  Pablo J. Zarco-Tejada,et al.  Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .

[117]  Alphus D. Wilson,et al.  Applications and Advances in Electronic-Nose Technologies , 2009, Sensors.

[118]  Abdul Salam,et al.  Internet of Things for Sustainable Human Health , 2019 .

[119]  Eldert J. van Henten,et al.  Proximal Gamma-Ray Spectroscopy to Predict Soil Properties Using Windows and Full-Spectrum Analysis Methods , 2013, Sensors.

[120]  Abdul Mounem Mouazen,et al.  Application of an on-line sensor to map soil packing density for site specific cultivation , 2016 .

[121]  J. Alex Thomasson,et al.  Ground-based sensing system for weed mapping in cotton , 2008 .

[122]  Francisca López Granados Weed detection for site-specific weed management: Mapping and real-time approaches , 2011 .

[123]  Suat Irmak,et al.  Nebraska Agricultural Water Management Demonstration Network (NAWMDN): integrating research and extension/outreach. , 2010 .

[124]  J. V. Stafford,et al.  Dynamic Sensing of Soil Pans , 1988 .

[125]  Understanding corn development: A key for successful crop management , 2010 .

[126]  Edward M. Barnes,et al.  Remote Sensing of Cotton Nitrogen Status Using the Canopy Chlorophyll Content Index (CCCI) , 2008 .

[127]  Mohammed R. Islam,et al.  Field Methods for Monitoring Solute Transport , 2005 .

[128]  Qian Du,et al.  Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture , 2013, Proceedings of the IEEE.

[129]  Peter Selsam,et al.  ACQUISITION AND AUTOMATED RECTIFICATION OF HIGH-RESOLUTION RGB AND NEAR-IR AERIAL PHOTOGRAPHS TO ESTIMATE PLANT BIOMASS AND SURFACE TOPOGRAPHY IN ARID AGRO-ECOSYSTEMS , 2016, Experimental Agriculture.

[130]  B. Jenkins,et al.  Development, Construction, and Field Evaluation of a Soil Compaction Profile Sensor , 2007 .

[131]  Viacheslav I. Adamchuk,et al.  Water and Nitrogen Effects on Active Canopy Sensor Vegetation Indices , 2011 .

[132]  S. Irmak,et al.  Performance of Frequency-Domain Reflectometer, Capacitance, and Psuedo-Transit Time-Based Soil Water Content Probes in Four Coarse-Textured Soils , 2005 .

[133]  Arno Ruckelshausen,et al.  BreedVision — A Multi-Sensor Platform for Non-Destructive Field-Based Phenotyping in Plant Breeding , 2013, Sensors.

[134]  Alessandro Rizzello,et al.  Sustainable Financial Partnerships for the SDGs: The Case of Social Impact Bonds , 2020, Sustainability.

[135]  C. Nansen,et al.  Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola , 2016, Precision Agriculture.

[136]  Barry J. Allred,et al.  Handbook of Agricultural Geophysics , 2016 .

[137]  Urs Schmidhalter,et al.  The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology , 2017, Sensors.

[138]  D. Ehlert,et al.  Widescale testing of the Crop-meter for site-specific farming , 2006, Precision Agriculture.

[139]  Xiaozhe Fan,et al.  The Future of Emerging IoT Paradigms: Architectures and Technologies , 2019 .

[140]  Jon Nielsen,et al.  Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? , 2016 .

[141]  A. Samal,et al.  Using targeted sampling to process multivariate soil sensing data , 2011 .

[142]  N. Coops,et al.  Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras , 2014 .

[143]  Viacheslav I. Adamchuk,et al.  Development of an NDIR CO2 Sensor-Based System for Assessing Soil Toxicity Using Substrate-Induced Respiration , 2015, Sensors.

[144]  F. Nex,et al.  UAV for 3D mapping applications: a review , 2014 .

[145]  Deng Lie,et al.  Effects of citrus tree-shape and spraying height of small unmanned aerial vehicle on droplet distribution , 2016 .

[146]  Arko Lucieer,et al.  HyperUAS—Imaging Spectroscopy from a Multirotor Unmanned Aircraft System , 2014, J. Field Robotics.

[147]  Molina Martínez,et al.  Software application for calculating solar radiation and equivalent evaporation in mobile devices , 2015 .

[148]  Mehmet C. Vuran,et al.  Internet of underground things in precision agriculture: Architecture and technology aspects , 2018, Ad Hoc Networks.

[149]  Irene Marzolff,et al.  Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco , 2012, Remote. Sens..

[150]  Abdul Salam Subsurface MIMO: A Beamforming Design in Internet of Underground Things for Digital Agriculture Applications , 2019, J. Sens. Actuator Networks.

[151]  David G. Schmale,et al.  Tracking the potato late blight pathogen in the atmosphere using unmanned aerial vehicles and Lagrangian modeling , 2011 .

[152]  S. L. Steinberg A Gauge to Measure Mass Flow Rate of Sap in Stems and Trunks of Woody Plants , 1989, Journal of the American Society for Horticultural Science.

[153]  J. Araus,et al.  Field high-throughput phenotyping: the new crop breeding frontier. , 2014, Trends in plant science.

[154]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[155]  Jörn Selbeck,et al.  Ranging Sensors for Vehicle-Based Measurement of Crop Stand and Orchard Parameters: A Review , 2011 .

[156]  R. Gebbers,et al.  Evaluating Spatially Resolved Influence of Soil and Tree Water Status on Quality of European Plum Grown in Semi-humid Climate , 2017, Front. Plant Sci..

[157]  Kazunobu Ishii,et al.  Correction of Low-altitude Thermal Images applied to estimating Soil Water Status , 2007 .

[158]  L. Bundy,et al.  Diagnostic Tests for Site‐Specific Nitrogen Recommendations for Winter Wheat , 2004 .

[159]  Mehmet C. Vuran,et al.  Smart underground antenna arrays: A soil moisture adaptive beamforming approach , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[160]  J. Baluja,et al.  Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV) , 2012, Irrigation Science.

[161]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[162]  Abdul Salam,et al.  Internet of Things for Sustainable Mining , 2019 .

[163]  S. Sankaran,et al.  Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review , 2015 .

[164]  Abdul Salam,et al.  Internet of Things in Sustainable Energy Systems , 2019 .

[165]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[166]  Xin Dong,et al.  Spatio-temporal soil moisture measurement with wireless underground sensor networks , 2010, 2010 The 9th IFIP Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[167]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[168]  Kenneth A. Sudduth,et al.  Sun Position and Cloud Effects on Reflectance and Vegetation Indices of Corn , 2010 .

[169]  E. V. Lukina,et al.  Improving Nitrogen Use Efficiency in Cereal Grain Production with Optical Sensing and Variable Rate Application , 2002 .

[170]  S. Friedman,et al.  Relationships between the Electrical and Hydrogeological Properties of Rocks and Soils , 2005 .

[171]  S. Irmak,et al.  Development and Application of a Performance and Operational Feasibility Guide to Facilitate Adoption of Soil Moisture Sensors , 2019, Sustainability.

[172]  Suat Irmak,et al.  Pulses in the sand: Impulse response analysis of wireless underground channel , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[173]  C. Daughtry,et al.  Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status , 2005, Precision Agriculture.

[174]  H. Nieto,et al.  Crop water stress maps for an entire growing season from visible and thermal UAV imagery , 2016 .

[175]  Saleh Taghvaeian,et al.  Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma , 2018, Sensors.

[176]  F. López-Granados,et al.  Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV , 2014 .

[177]  Umit Karabiyik,et al.  A Cooperative Overlay Approach at the Physical Layer of Cognitive Radio for Digital Agriculture , 2019 .

[178]  Mehmet C. Vuran,et al.  Wireless underground channel diversity reception with multiple antennas for internet of underground things , 2017, 2017 IEEE International Conference on Communications (ICC).

[179]  Erich-Christian Oerke,et al.  Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet , 2011, Precision Agriculture.

[180]  E. F. Wallihan,et al.  Portable Reflectance Meter for Estimating Chlorophyll Concentrations in Leaves1 , 1973 .

[181]  Abdul Salam,et al.  Internet of Things for Water Sustainability , 2019, Internet of Things.

[182]  James S. Schepers,et al.  Use of a virtual-reference concept to interpret active crop canopy sensor data , 2013, Precision Agriculture.

[183]  Roger T Hanlon,et al.  Use of commercial off-the-shelf digital cameras for scientific data acquisition and scene-specific color calibration. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[184]  Daran R. Rudnick,et al.  Performance Analysis of Capacitance and Electrical Resistance-Type Soil Moisture Sensors in a Silt Loam Soil , 2015 .

[185]  Lav R. Khot,et al.  Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand , 2015, Comput. Electron. Agric..

[186]  Georg Bareth,et al.  NON-DESTRUCTIVE MONITORING OF RICE BY HYPERSPECTRAL IN-FIELD SPECTROMETRY AND UAV-BASED REMOTE SENSING: CASE STUDY OF FIELD-GROWN RICE IN NORTH RHINE-WESTPHALIA, GERMANY , 2016 .

[187]  W. Raun,et al.  In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index , 2006 .

[188]  Gerhard Lange,et al.  Geophysics / Geodesy: Handbuch zur Erkundung des Untergrundes von Deponien und Altlasten , 1999 .

[189]  Mehmet C. Vuran,et al.  Towards Internet of Underground Things in smart lighting: A statistical model of wireless underground channel , 2017, 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC).

[190]  E. M. Schetselaar,et al.  Guidelines for radioelement mapping using gamma ray spectrometry data : also as open access e-book , 2003 .

[191]  S. Jones,et al.  A Review of Advances in Dielectric and Electrical Conductivity Measurement in Soils Using Time Domain Reflectometry , 2003 .

[192]  Graciela Metternicht,et al.  Remote sensing of soil salinity: potentials and constraints , 2003 .

[193]  Marek G Zreda,et al.  Quantifying mesoscale soil moisture with the cosmic-ray rover , 2013 .

[194]  David C. Slaughter,et al.  Autonomous robotic weed control systems: A review , 2008 .

[195]  D. Goodin,et al.  Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries , 2016, Plant Methods.

[196]  D. Puchberger-Enengl,et al.  A mobile lab-on-a-chip device for on-site soil nutrient analysis , 2017, Precision Agriculture.