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Volume 3, Issue 1
  • ISSN: 2666-7312
  • E-ISSN: 2666-7339

Abstract

This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture.

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2024-02-07
2025-02-17

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References

  1. SchnebeleE. TanyuB.F. CervoneG. WatersN. Review of remote sensing methodologies for pavement management and assessment.Eur. Trans. Res. Rev.201572710.1007/s12544‑015‑0156‑6
     [Google Scholar]
  2. EdokossiK. CalabiaA. JinS. MolinaI. GNSS-reflectometry and remote sensing of soil moisture: A review of measurement techniques, methods, and applications.Remote Sens.202012461410.3390/rs12040614
     [Google Scholar]
  3. YuanQ. ShenH. LiT. Deep learning in environmental remote sensing: Achievements and challenges.Remote Sens. Environ.202024111171610.1016/j.rse.2020.111716
     [Google Scholar]
  4. SteadD. DonatiD. WolterA. SturzeneggerM. Application of remote sensing to the investigation of rock slopes: Experience gained and lessons learned.ISPRS Int. J. Geoinf.20198729610.3390/ijgi8070296
     [Google Scholar]
  5. ChengG. LiZ. YaoX. GuoL. WeiZ. Remote sensing image scene classification using bag of convolutional features.IEEE Geosci. Remote Sens. Lett.201714101735173910.1109/LGRS.2017.2731997
     [Google Scholar]
  6. AldabaaA.A.A. WeindorfD.C. ChakrabortyS. SharmaA. LiB. Combination of proximal and remote sensing methods for rapid soil salinity quantification.Geoderma2015239-240344610.1016/j.geoderma.2014.09.011
     [Google Scholar]
  7. LyonsM.B. KeithD.A. PhinnS.R. MasonT.J. ElithJ. A comparison of resampling methods for remote sensing classification and accuracy assessment.Remote Sens. Environ.201820814515310.1016/j.rse.2018.02.026
     [Google Scholar]
  8. LawleyV. LewisM. ClarkeK. OstendorfB. Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review.Ecol. Indic.2016601273128310.1016/j.ecolind.2015.03.021
     [Google Scholar]
  9. XuW. ChenP. ZhanY. ChenS. ZhangL. LanY. Cotton yield estimation model based on machine learning using time series UAV remote sensing data.Int. J. Appl. Earth Obs. Geoinf.202110410251110.1016/j.jag.2021.102511
     [Google Scholar]
  10. Mueller-WarrantG.W. WhittakerG.W. BanowetzG.M. GriffithS.M. BarnhartB.L. Methods for improving accuracy and extending results beyond periods covered by traditional ground-truth in remote sensing classification of a complex landscape.Int. J. Appl. Earth Obs. Geoinf.20153811512810.1016/j.jag.2015.01.001
     [Google Scholar]
  11. MateseA. ToscanoP. Di GennaroS. Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture.Remote Sens.2015732971299010.3390/rs70302971
     [Google Scholar]
  12. TothC. JóźkówG. Remote sensing platforms and sensors: A survey.ISPRS J. Photogramm. Remote Sens.2016115223610.1016/j.isprsjprs.2015.10.004
     [Google Scholar]
  13. LiuC. CaoY. YangC. ZhouY. AiM. Pattern identification and analysis for the traditional village using low altitude UAV-borne remote sensing: Multifeatured geospatial data to support rural landscape investigation, documentation and management.J. Cult. Herit.20204418519510.1016/j.culher.2019.12.013
     [Google Scholar]
  14. GuW. LvZ. HaoM. Change detection method for remote sensing images based on an improved Markov random field.Multimedia Tools Appl.20177617177191773410.1007/s11042‑015‑2960‑3
     [Google Scholar]
  15. LinL. A review of remote sensing in flood assessment.Fifth International Conference on Agro-Geoinformatics (Agro- Geoinformatics).Tianjin, China20161820July; 1-4.
     [Google Scholar]
  16. ChenG. LiS. KnibbsL.D. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information.Sci. Total Environ.2018636526010.1016/j.scitotenv.2018.04.25129702402
     [Google Scholar]
  17. MengX. ShenH. LiH. ZhangL. FuR. Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges.Inf. Fusion20194610211310.1016/j.inffus.2018.05.006
     [Google Scholar]
  18. ShaoZ. CaiJ. Remote sensing image fusion with deep convolutional neural network.IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.20181151656166910.1109/JSTARS.2018.2805923
     [Google Scholar]
  19. WangR. GamonJ.A. Remote sensing of terrestrial plant biodiversity.Remote Sens. Environ.201923111121810.1016/j.rse.2019.111218
     [Google Scholar]
  20. LiZ. WangY. ZhangN. Deep learning-based object detection techniques for remote sensing images: A survey.Remote Sens.20221410238510.3390/rs14102385
     [Google Scholar]
  21. AasenH. HonkavaaraE. LucieerA. Zarco-TejadaP. Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures, and data correction workflows.Remote Sens.2018107109110.3390/rs10071091
     [Google Scholar]
  22. KaputaD.S. BauchT. RobertsC. McKeownD. FooteM. SalvaggioC. Mx-1: A new multi-modal remote sensing UAS payload with high accuracy GPS and IMU.IEEE Systems and Technologies for Remote Sensing Applications Through Unmanned Aerial Systems (STRATUS).Rochester, NY, USA.20192527Feb; 1-4.
     [Google Scholar]
  23. MaesW.H. SteppeK. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture.Trends Plant Sci.201924215216410.1016/j.tplants.2018.11.00730558964
     [Google Scholar]
  24. ShakhatrehH SawalmehAH Al-FuqahaA Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges.IEEE Access201974857263410.1109/ACCESS.2019.2909530
     [Google Scholar]
  25. ZhaoT. DollD. WangD. ChenY. A new framework for UAV-based remote sensing data processing and its application in almond water stress quantification.International Conference on Unmanned Aircraft Systems (ICUAS)Miami, FL, USA20171316June; 1794-9.
     [Google Scholar]
  26. ZhangC. ValenteJ. KooistraL. GuoL. WangW. Orchard management with small unmanned aerial vehicles: A survey of sensing and analysis approaches.Precis. Agric.20212262007205210.1007/s11119‑021‑09813‑y
     [Google Scholar]
  27. NorzailawatiM.N. AliasA. AkmaR.S. Designing zoning of remote sensing drones for urban applications: A review.Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci2016XLIB613113810.5194/isprs‑archives‑XLI‑B6‑131‑2016
     [Google Scholar]
  28. AsadzadehS. OliveiraW.J. Souza FilhoC.R. UAV-based remote sensing for the petroleum industry and environmental monitoring: State-of-the-art and perspectives.J. Petrol. Sci. Eng.202220810963310.1016/j.petrol.2021.109633
     [Google Scholar]
  29. LiaoX. ZhangY. SuF. YueH. DingZ. LiuJ. UAVs surpassing satellites and aircraft in remote sensing over China.Int. J. Remote Sens.201839217138715310.1080/01431161.2018.1515511
     [Google Scholar]
  30. YangZ. YuX. DedmanS. UAV remote sensing applications in marine monitoring: Knowledge visualization and review.Sci. Total Environ.2022838Pt 115593910.1016/j.scitotenv.2022.15593935577092
     [Google Scholar]
  31. WestH. QuinnN. HorswellM. Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities.Remote Sens. Environ.201923211129110.1016/j.rse.2019.111291
     [Google Scholar]
  32. ZhangK. KimballJ.S. RunningS.W. A review of remote sensing based actual evapotranspiration estimation.WIREs. Water20163683485310.1002/wat2.1168
     [Google Scholar]
  33. XiaoJ. ChevallierF. GomezC. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years.Remote Sens. Environ.201923311138310.1016/j.rse.2019.111383
     [Google Scholar]
  34. LevinN. KybaC.C.M. ZhangQ. Remote sensing of night lights: A review and an outlook for the future.Remote Sens. Environ.202023711144310.1016/j.rse.2019.111443
     [Google Scholar]
  35. SishodiaR.P. RayR.L. SinghS.K. Applications of remote sensing in precision agriculture: A review.Remote Sens.20201219313610.3390/rs12193136
     [Google Scholar]
  36. SmithW.K. DannenbergM.P. YanD. Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities.Remote Sens. Environ.201923311140110.1016/j.rse.2019.111401
     [Google Scholar]
  37. WeissM. JacobF. DuveillerG. Remote sensing for agricultural applications: A meta-review.Remote Sens. Environ.202023611140210.1016/j.rse.2019.111402
     [Google Scholar]
  38. WangL. JiaM. YinD. TianJ. A review of remote sensing for mangrove forests: 1956-2018.Remote Sens. Environ.201923111122310.1016/j.rse.2019.111223
     [Google Scholar]
  39. MegahedY. CabralP. SilvaJ. CaetanoM. Land cover mapping analysis and urban growth modelling using remote sensing techniques in Greater Cairo Region-Egypt.ISPRS Int. J. Geoinf.2015431750176910.3390/ijgi4031750
     [Google Scholar]
  40. PajaresG. Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs).Photogramm. Eng. Remote Sensing201581428133010.14358/PERS.81.4.281
     [Google Scholar]
  41. BhardwajA. SamL. Akanksha, Martín-Torres FJ, Kumar R. UAVs as remote sensing platform in glaciology: Present applications and future prospects.Remote Sens. Environ.201617519620410.1016/j.rse.2015.12.029
     [Google Scholar]
  42. Mohd NoorN. AbdullahA. HashimM. Remote sensing UAV/drones and its applications for urban areas: A review.IOP Conf. Ser. Earth Environ. Sci.201816901200310.1088/1755‑1315/169/1/012003
     [Google Scholar]
  43. YaoH. QinR. ChenX. Unmanned aerial vehicle for remote sensing applications-A review.Remote Sens.20191112144310.3390/rs11121443
     [Google Scholar]
  44. TangL. ShaoG. Drone remote sensing for forestry research and practices.J. For. Res.201526479179710.1007/s11676‑015‑0088‑y
     [Google Scholar]
  45. FerozS. Abu DabousS. Uav-based remote sensing applications for bridge condition assessment.Remote Sens.2021139180910.3390/rs13091809
     [Google Scholar]
  46. KlemasV.V. Coastal and environmental remote sensing from unmanned aerial vehicles: An overview.J. Coast. Res.201531551260126710.2112/JCOASTRES‑D‑15‑00005.1
     [Google Scholar]
  47. ZhongY. WangX. XuY. Mini-UAV-borne hyperspectral remote sensing: From observation and processing to applications.IEEE Geosci. Remote Sens. Mag.201864466210.1109/MGRS.2018.2867592
     [Google Scholar]
  48. SiewertM.B. OlofssonJ. UAV reveals substantial but heterogeneous effects of herbivores on Arctic vegetation.Sci. Rep.20211111946810.1038/s41598‑021‑98497‑534593844
     [Google Scholar]
  49. JungJ. MaedaM. ChangA. BhandariM. AshapureA. Landivar-BowlesJ. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems.Curr. Opin. Biotechnol.202170152210.1016/j.copbio.2020.09.00333038780
     [Google Scholar]
  50. HanX. ThomassonJ.A. BagnallG.C. Measurement and calibration of plant-height from fixed-wing UAV images.Sensors20181812409210.3390/s1812409230469545
     [Google Scholar]
  51. NdukuL. MunghemezuluC. Mashaba-MunghemezuluZ. Global research trends for unmanned aerial vehicle remote sensing application in wheat crop monitoring.Geomatics20233111513610.3390/geomatics3010006
     [Google Scholar]
  52. BoursianisA.D. PapadopoulouM.S. DiamantoulakisP. Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review.Internet of Things20221810018710.1016/j.iot.2020.100187
     [Google Scholar]
  53. SinghP. Hyperspectral remote sensing in precision agriculture: Present status, challenges, and future trends. In: Hyperspectral remote sensing.Elsevier202012114610.1016/B978‑0‑08‑102894‑0.00009‑7
     [Google Scholar]
  54. XuY. YangY. ChenX. LiuY. Bibliometric analysis of global NDVI research trends from 1985 to 2021.Remote Sens.20221416396710.3390/rs14163967
     [Google Scholar]
  55. LanY ChenS.  Current status and trends of plant protection UAV and its spraying technology in China. Int J Precis Agric Aviat201811910.33440/j.ijpaa.20180101.0002
     [Google Scholar]
  56. BeamishA. RaynoldsM.K. EpsteinH. Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook.Remote Sens. Environ.202024611187210.1016/j.rse.2020.111872
     [Google Scholar]
  57. WangL. ZhangG. WangZ. LiuJ. ShangJ. LiangL. Bibliometric analysis of remote sensing research trend in crop growth monitoring: A case study in China.Remote Sens.201911780910.3390/rs11070809
     [Google Scholar]
  58. PeñaJ. Torres-SánchezJ. Serrano-PérezA. de CastroA. López-GranadosF. Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution.Sensors20151535609562610.3390/s15030560925756867
     [Google Scholar]
  59. ZhiY. FuZ. SunX. YuJ. Security and privacy issues of UAV: A survey.Mob. Netw. Appl.20202519510110.1007/s11036‑018‑1193‑x
     [Google Scholar]
  60. IdriesA. MohamedN. JawharI. MohamedF. Al-JaroodiJ. Challenges of developing UAV applications: A project management view.International Conference on Industrial Engineering and Operations Management (IEOM)Dubai, United Arab Emirates03-05 Mar2015110
     [Google Scholar]
  61. HuangY. ReddyK.N. FletcherR.S. PenningtonD. UAV low-altitude remote sensing for precision weed management.Weed Technol.20183212610.1017/wet.2017.89
     [Google Scholar]
  62. AdãoT. HruškaJ. PáduaL. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry.Remote Sens.2017911111010.3390/rs9111110
     [Google Scholar]
  63. JawharI. MohamedN. Al-JaroodiJ. AgrawalD.P. ZhangS. Communication and networking of UAV-based systems: Classification and associated architectures.J. Netw. Comput. Appl.2017849310810.1016/j.jnca.2017.02.008
     [Google Scholar]
  64. ZhangH. WangL. TianT. YinJ. A review of unmanned aerial vehicle low-altitude remote sensing (UAV-LARS) use in agricultural monitoring in China.Remote Sens.2021136122110.3390/rs13061221
     [Google Scholar]
  65. HuZ. BaiZ. YangY. ZhengZ. BianK. SongL. UAV aided aerial-ground IoT for air quality sensing in smart city: Architecture, technologies, and implementation.IEEE Netw.2019332142210.1109/MNET.2019.1800214
     [Google Scholar]
  66. GageikN BenzP MontenegroS Obstacle detection and collision avoidance for a UAV with complementary low-cost sensors.IEEE Access2015359960910.1109/ACCESS.2015.2432455
     [Google Scholar]
  67. ErdeljM. KrólM. NatalizioE. Wireless sensor networks and multi-uav systems for natural disaster management.Comput. Netw.2017124728610.1016/j.comnet.2017.05.021
     [Google Scholar]
  68. TammingaA. HugenholtzC. EatonB. LapointeM. Hyperspatial remote sensing of channel reach morphology and hydraulic fish habitat using an Unmanned Aerial Vehicle (UAV): A first assessment in the context of river research and management.River Res. Appl.201531337939110.1002/rra.2743
     [Google Scholar]
  69. Hernández-VegaJ-I. VarelaE.R. RomeroN.H. Hernández-SantosC. CuevasJ.L.S. GorhamD.G.P. Internet of Things (IoT) for monitoring air pollutants with an Unmanned Aerial Vehicle (UAV) in a smart city.In: Smart Technologies.World Scientific Publishing Company201810.1007/978‑3‑319‑73323‑4_11
     [Google Scholar]
  70. GabrlikP. The use of direct georeferencing in aerial photogrammetry with micro UAV.IFAC-PapersOnLine201548438038510.1016/j.ifacol.2015.07.064
     [Google Scholar]
  71. RenH. ZhaoY. XiaoW. HuZ. A review of UAV monitoring in mining areas: current status and future perspectives.Int. J. Coal Sci. Technol.20196332033310.1007/s40789‑019‑00264‑5
     [Google Scholar]
  72. NevalainenO. HonkavaaraE. TuominenS. Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging.Remote Sens.20179318510.3390/rs9030185
     [Google Scholar]
  73. LiF. DuY. SunX. ZhaoW. Sensing performance assessment of twisted CFRP with embedded fiber Bragg grating sensors subjected to monotonic and fatigue loading.Sens. Actuators A Phys.201827115316110.1016/j.sna.2018.01.027
     [Google Scholar]
  74. YangG. UAV based multi-load remote sensing technologies for wheat breeding information acquirement.Nongye Gongcheng Xuebao20153121184190
     [Google Scholar]
  75. AchilleC. AdamiA. ChiariniS. UAV-based photogrammetry and integrated technologies for architectural applications-methodological strategies for the after-quake survey of vertical structures in Mantua (Italy).Sensors2015157155201553910.3390/s15071552026134108
     [Google Scholar]
  76. OlsonD. AndersonJ. Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture.Agron. J.2021113297199210.1002/agj2.20595
     [Google Scholar]
  77. LarsonM.D. Simic MilasA. VincentR.K. EvansJ.E. Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio.Int. J. Remote Sens.20183915-165472548910.1080/01431161.2018.1465616
     [Google Scholar]
  78. LuoF. JiangC. YuS. WangJ. LiY. RenY. Stability of cloud-based UAV systems supporting big data acquisition and processing.IEEE Trans. Cloud Comput.20197386687710.1109/TCC.2017.2696529
     [Google Scholar]
  79. KimP. ParkJ. ChoY.K. KangJ. UAV-assisted autonomous mobile robot navigation for as-is 3D data collection and registration in cluttered environments.Autom. Construct.201910610291810.1016/j.autcon.2019.102918
     [Google Scholar]
  80. IslamA. ShinS.Y. BUAV: A blockchain based secure UAV-assisted data acquisition scheme in Internet of Things.J. Commun. Netw.201921549150210.1109/JCN.2019.000050
     [Google Scholar]
  81. WangZ. LiuR. LiuQ. ThompsonJ.S. KadochM. Energy-efficient data collection and device positioning in UAV-assisted IoT.IEEE Internet Things J.2020721122113910.1109/JIOT.2019.2952364
     [Google Scholar]
  82. HyyppäE. YuX. KaartinenH. Comparison of backpack, handheld, under-canopy UAV, and above-canopy UAV laser scanning for field reference data collection in boreal forests.Remote Sens.20201220332710.3390/rs12203327
     [Google Scholar]
  83. YiM. WangX. LiuJ. ZhangY. BaiB. Deep reinforcement learning for fresh data collection in UAV-assisted IoT networks.IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Toronto, ON, Canada.July; 716-21.2020060910.1109/INFOCOMWKSHPS50562.2020.9162896
     [Google Scholar]
  84. ZhangY. MouZ. GaoF. XingL. JiangJ. HanZ. Hierarchical deep reinforcement learning for backscattering data collection with multiple UAVs.IEEE Internet Things J.2021853786380010.1109/JIOT.2020.3024666
     [Google Scholar]
  85. PopescuD. StoicanF. StamatescuG. IchimL. DraganaC. Advanced UAV–WSN system for intelligent monitoring in precision agriculture.Sensors202020381710.3390/s2003081732028736
     [Google Scholar]
  86. IslamA ShinSY Bus: A blockchain-enabled data acquisition scheme with the assistance of uav swarm in internet of things.IEEE Access201971032314910.1109/ACCESS.2019.2930774
     [Google Scholar]
  87. AhmedO.S. ShemrockA. ChabotD. Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle.Int. J. Remote Sens.2017388-102037205210.1080/01431161.2017.1294781
     [Google Scholar]
  88. SauraJ.R. Reyes-MenendezA. Palos-SanchezP. Mapping multispectral Digital Images using a Cloud Computing software: applications from UAV images.Heliyon201952e0127710.1016/j.heliyon.2019.e01277
     [Google Scholar]
  89. Radoglou-GrammatikisP. SarigiannidisP. LagkasT. MoscholiosI. A compilation of UAV applications for precision agriculture.Comput. Netw.202017210714810.1016/j.comnet.2020.107148
     [Google Scholar]
  90. AbdullahiH.S. MahieddineF. SheriffR.E. Technology Impact on Agricultural Productivity: A Review of Precision Agriculture Using Unmanned Aerial Vehicles.International Conference on Wireless and Satellite Systems10.1007/978‑3‑319‑25479‑1_29
     [Google Scholar]
  91. ChristiansenM. LaursenM. JørgensenR. SkovsenS. GislumR. Designing and testing a UAV mapping system for agricultural field surveying.Sensors20171712270310.3390/s1712270329168783
     [Google Scholar]
  92. SarronJ. MalézieuxÉ. SanéC. FayeÉ. Mango yield mapping at the orchard scale based on tree structure and land cover assessed by UAV.Remote Sens.20181012190010.3390/rs10121900
     [Google Scholar]
  93. StöckerC. BennettR. KoevaM. NexF. ZevenbergenJ. Scaling up UAVs for land administration: Towards the plateau of productivity.Land Use Policy202211410593010.1016/j.landusepol.2021.105930
     [Google Scholar]
  94. SonaG. PassoniD. PintoL. UAV multispectral survey to map soil and crop for precision farming applications.Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci2016XLIB11023102910.5194/isprs‑archives‑XLI‑B1‑1023‑2016
     [Google Scholar]
  95. DelavarpourN. KoparanC. NowatzkiJ. BajwaS. SunX. A technical study on UAV characteristics for precision agriculture applications and associated practical challenges.Remote Sens.2021136120410.3390/rs13061204
     [Google Scholar]
  96. HafeezA. Implementation of drone technology for farm monitoring & pesticide spraying: A review.Inf. Process. Agric.2022102192203
     [Google Scholar]
  97. AmarasingamN. Ashan SalgadoeA.S. PowellK. GonzalezL.F. NatarajanS. A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops.Remote Sens. Appl. Soc. Environ.20222610071210.1016/j.rsase.2022.100712
     [Google Scholar]
  98. NhamoL. MagidiJ. NyamugamaA. Prospects of improving agricultural and water productivity through unmanned aerial vehicles.Agriculture202010725610.3390/agriculture10070256
     [Google Scholar]
  99. SinghP.K. SharmaA. An intelligent WSN-UAV-based IoT framework for precision agriculture application.Comput. Electr. Eng.202210010791210.1016/j.compeleceng.2022.107912
     [Google Scholar]
  100. DuttaP.K. MitraS. Application of agricultural drones and iot to understand food supply chain during post COVID‐19.Agricultural Informatics.1st ed ChoudhuryA. BiswasA. PrateekM. ChakrabartiA. Wiley2021678710.1002/9781119769231.ch4
     [Google Scholar]
  101. TsourosD.C. BibiS. SarigiannidisP.G. A review on UAV-based applications for precision agriculture.Information2019101134910.3390/info10110349
     [Google Scholar]
  102. SouzaC.H.W. LamparelliR.A.C. RochaJ.V. MagalhãesP.S.G. Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images.Comput. Electron. Agric.2017143495610.1016/j.compag.2017.10.006
     [Google Scholar]
  103. HonradoJ.L.E. SolpicoD.B. FavilaC.M. TongsonE. TangonanG.L. LibatiqueN.J. UAV imaging with low-cost multispectral imaging system for precision agriculture applications.IEEE Global Humanitarian Technology Conference (GHTC)San Jose, CA, USAOct; 1-720171922
     [Google Scholar]
  104. AslanM.F. DurduA. SabanciK. RopelewskaE. GültekinS.S. A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses.Appl. Sci. 2022123104710.3390/app12031047
     [Google Scholar]
  105. NorasmaC.Y.N. FadzilahM.A. RoslinN.A. ZanariahZ.W.N. TarmidiZ. CandraF.S. Unmanned aerial vehicle applications in agriculture.IOP Conf. Series: Mater. Sci. Eng.2019012063
     [Google Scholar]
  106. RadjawaliI. PyeO. FlitnerM. Recognition through reconnaissance? Using drones for counter-mapping in Indonesia.In: Decentring Land Grabbing.Routledge201912013610.4324/9781351134873‑6
     [Google Scholar]
  107. MessinaG. PeñaJ.M. VizzariM. ModicaG. A comparison of UAV and satellites multispectral imagery in monitoring onion crop. An application in the ‘Cipolla Rossa di Tropea’(Italy).Remote Sens.20201220342410.3390/rs12203424
     [Google Scholar]
  108. DuanT. ChapmanS.C. GuoY. ZhengB. Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle.Field Crops Res.2017210718010.1016/j.fcr.2017.05.025
     [Google Scholar]
  109. Di GennaroS.F. BattistonE. Marco DiS. Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex.Phytopathol. Mediterr.2016552262275
     [Google Scholar]
  110. Al-AliZ.M. AbdullahM.M. AsadallaN.B. GholoumM. A comparative study of remote sensing classification methods for monitoring and assessing desert vegetation using a UAV-based multispectral sensor.Environ. Monit. Assess.2020192638910.1007/s10661‑020‑08330‑132447581
     [Google Scholar]
  111. van IerselW. StraatsmaM. AddinkE. MiddelkoopH. Monitoring height and greenness of non-woody floodplain vegetation with UAV time series.ISPRS J. Photogramm. Remote Sens.201814111212310.1016/j.isprsjprs.2018.04.011
     [Google Scholar]
  112. MengmengD. NoboruN. AtsushiI. YukinoriS. Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle.Int. J. Agric. Biol. Eng.201710511310.25165/j.ijabe.20171005.3180
     [Google Scholar]
  113. ChabotD. Trends in drone research and applications as the Journal of Unmanned Vehicle Systems turns five.J. Unmanned Veh. Syst.201861vixv10.1139/juvs‑2018‑0005
     [Google Scholar]
  114. Rueda-AyalaV. PeñaJ. HöglindM. Bengochea-GuevaraJ. AndújarD. Comparing UAV-based technologies and RGB-D reconstruction methods for plant height and biomass monitoring on grass ley.Sensors201919353510.3390/s1903053530696014
     [Google Scholar]
  115. JuradoJ.M. OrtegaL. CubillasJ.J. FeitoF.R. Multispectral mapping on 3D models and multi-temporal monitoring for individual characterization of olive trees.Remote Sens.2020127110610.3390/rs12071106
     [Google Scholar]
  116. López-GranadosF. Torres-SánchezJ. De CastroA.I. Serrano-PérezA. Mesas-CarrascosaF.J. PeñaJ.M. Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery.Agron. Sustain. Dev.20163646710.1007/s13593‑016‑0405‑7
     [Google Scholar]
  117. SalhaouiM. Guerrero-GonzálezA. AriouaM. OrtizF.J. El OualkadiA. TorregrosaC.L. Smart industrial iot monitoring and control system based on UAV and cloud computing applied to a concrete plant.Sensors20191915331610.3390/s1915331631357720
     [Google Scholar]
  118. ZhangY. WuH. YangW. Forests growth monitoring based on tree canopy 3D reconstruction using UAV aerial photogrammetry.Forests20191012105210.3390/f10121052
     [Google Scholar]
  119. FabbriS. GrottoliE. ArmaroliC. CiavolaP. Using high-spatial resolution UAV-derived data to evaluate vegetation and geomorphological changes on a dune field involved in a restoration endeavour.Remote Sens.20211310198710.3390/rs13101987
     [Google Scholar]
  120. ZhangY. ChenD. WangS. TianL. A promising trend for field information collection: An air-ground multi-sensor monitoring system.Inf. Process. Agric.20185222423310.1016/j.inpa.2018.02.002
     [Google Scholar]
  121. PáduaL. GuimarãesN. AdãoT. SousaA. PeresE. SousaJ.J. Effectiveness of sentinel-2 in multi-temporal post-fire monitoring when compared with UAV imagery.ISPRS Int. J. Geoinf.20209422510.3390/ijgi9040225
     [Google Scholar]
  122. TomsettC. LeylandJ. Remote sensing of river corridors: A review of current trends and future directions.River Res. Appl.201935777980310.1002/rra.3479
     [Google Scholar]
  123. DasS. ChapmanS. ChristopherJ. UAV-thermal imaging: A technological breakthrough for monitoring and quantifying crop abiotic stress to help sustain productivity on sodic soils - A case review on wheat.Remote Sens. Appl. Soc. Environ.20212310058310.1016/j.rsase.2021.100583
     [Google Scholar]
  124. ShahS.A. LakhoG.M. KeerioH.A. Application of drone surveillance for advance agriculture monitoring by android application using convolution neural network.Agronomy2023137176410.3390/agronomy13071764
     [Google Scholar]
  125. DainelliR. ToscanoP. Di GennaroS.F. MateseA. Recent advances in unmanned aerial vehicle forest remote sensing-A systematic review. part I: A general framework.Forests202112332710.3390/f12030327
     [Google Scholar]
  126. ZhouX. YangL. WangW. ChenB. Uav data as an alternative to field sampling to monitor vineyards using machine learning based on uav/sentinel-2 data fusion.Remote Sens.202113345710.3390/rs13030457
     [Google Scholar]
  127. SaganV. MaimaitijiangM. SidikeP. UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermomap cameras.Remote Sens.201911333010.3390/rs11030330
     [Google Scholar]
  128. ThollD. HossainO. WeinholdA. RöseU.S.R. WeiQ. Trends and applications in plant volatile sampling and analysis.Plant J.2021106231432510.1111/tpj.1517633506558
     [Google Scholar]
  129. ZhuW. SunZ. HuangY. Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping.Precis. Agric.20212261768180210.1007/s11119‑021‑09811‑0
     [Google Scholar]
  130. BerieH.T. BurudI. Application of unmanned aerial vehicles in earth resources monitoring: Focus on evaluating potentials for forest monitoring in Ethiopia.Eur. J. Remote Sens.201851132633510.1080/22797254.2018.1432993
     [Google Scholar]
  131. NeupaneK. Baysal-GurelF. Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: A review.Remote Sens.20211319384110.3390/rs13193841
     [Google Scholar]
  132. MonteiroA. SantosS. Sustainable approach to weed management: The role of precision weed management.Agronomy202212111810.3390/agronomy12010118
     [Google Scholar]
  133. MattiviP. PappalardoS.E. NikolićN. Can commercial low-cost drones and open-source GIS technologies be suitable for semi-automatic weed mapping for smart farming? A case study in NE Italy.Remote Sens.20211310186910.3390/rs13101869
     [Google Scholar]
  134. PedersenS.M. LindK.M. Precision agriculture - from mapping to site-specific application.In: Precision Agriculture: Technology and Economic Perspectives.Springer201710.1007/978‑3‑319‑68715‑5_1
     [Google Scholar]
  135. Said MohamedE. BelalA. Kotb Abd-ElmabodS. El-ShirbenyM.A. GadA. ZahranM.B. Smart farming for improving agricultural management.Egypt. J. Remote Sens. Space Sci.202124397198110.1016/j.ejrs.2021.08.007
     [Google Scholar]
  136. TianH. WangT. LiuY. QiaoX. LiY. Computer vision technology in agricultural automation -A review.Inf. Process. Agric.20207111910.1016/j.inpa.2019.09.006
     [Google Scholar]
  137. AbrahamsM. SibandaM. DubeT. ChimonyoV.G.P. MabhaudhiT. A systematic review of UAV applications for mapping neglected and underutilised crop species’ spatial distribution and health.Remote Sens.20231519467210.3390/rs15194672
     [Google Scholar]
  138. LyuX. LiX. DangD. DouH. WangK. LouA. Unmanned aerial vehicle (UAV) remote sensing in grassland ecosystem monitoring: A systematic review.Remote Sens.2022145109610.3390/rs14051096
     [Google Scholar]
  139. HallE.C. LaraM.J. Multisensor UAS mapping of Plant Species and Plant Functional Types in Midwestern Grasslands.Remote Sens.20221414345310.3390/rs14143453
     [Google Scholar]
  140. MacedoF.L. NóbregaH. de FreitasJ.G.R. Estimation of productivity and above-ground biomass for corn (Zea mays) via vegetation indices in madeira island.Agriculture2023136111510.3390/agriculture13061115
     [Google Scholar]
  141. PengM. HanW. LiC. YaoX. ShaoG. Modeling the daytime net primary productivity of maize at the canopy scale based on UAV multispectral imagery and machine learning.J. Clean. Prod.202236713304110.1016/j.jclepro.2022.133041
     [Google Scholar]
  142. QiaoL. GaoD. ZhaoR. Improving estimation of LAI dynamic by fusion of morphological and vegetation indices based on UAV imagery.Comput. Electron. Agric.202219210660310.1016/j.compag.2021.106603
     [Google Scholar]
  143. OrndahlK.M. EhlersL.P.W. HerrigesJ.D. PernickR.E. HebblewhiteM. GoetzS.J. Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles.Arct. Sci.202284AS-2021AS-004410.1139/AS‑2021‑0044
     [Google Scholar]
  144. BeniaichA. SilvaM.L.N. GuimarãesD.V. UAV-based vegetation monitoring for assessing the impact of soil loss in olive orchards in Brazil.Geoderma Reg.202230e0054310.1016/j.geodrs.2022.e00543
     [Google Scholar]
  145. SangjanW. Carpenter-BoggsL.A. HudsonT.D. SankaranS. Pasture productivity assessment under mob grazing and fertility management using satellite and uas imagery.Drones20226923210.3390/drones6090232
     [Google Scholar]
  146. LukasV. HuňadyI. KintlA. Using UAV to identify the optimal vegetation index for yield prediction of oil seed rape (Brassica napus L.) at the flowering stage.Remote Sens.20221419495310.3390/rs14194953
     [Google Scholar]
/content/journals/cam/10.2174/0126667312288014240129080801
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Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs)
Curr. Appl. Mat. 3, e070224226752 (2024); https://doi.org/10.2174/0126667312288014240129080801
/content/journals/cam/10.2174/0126667312288014240129080801
/content/journals/cam/10.2174/0126667312288014240129080801
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  • Article Type:     Review Article
Keyword(s): blockchain technology; cloud-based systems; robot navigation; secure data transfer; soil analysis; Unmanned aerial vehicles

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