Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Review Articles

Vol. 12 No. sp1 (2025): Recent Advances in Agriculture by Young Minds - II

Exploring the impact of drone technology on agricultural practices: A bibliometric review

DOI
https://doi.org/10.14719/pst.10165
Submitted
20 June 2025
Published
26-08-2025 — Updated on 23-09-2025
Versions

Abstract

Drone technology has emerged as a transformative tool in agriculture, yet its overall impact lacks a thorough bibliometric evaluation. The study offers a detailed analysis of its applications, advantages and the challenges faced in agricultural practices. Drones or unmanned aerial vehicles (UAVs), have become essential tools in precision agriculture, enabling real-time monitoring, diseases identification, yields estimation and the precise application of fertilizers and pesticides. By integrating advanced imaging technologies and machine learning, drones enhance decision-making and optimize resource utilization in farming operations. This research employs a bibliometric approach using Biblioshiny and VOSviewer to analyze 375 articles from the Scopus database (2011–2025),
identifying key research trends, influential authors and thematic clusters. The findings reveal a significant rise in agricultural drone research, particularly after 2019, driven by technology innovations and a global shift toward sustainable farming practices. Despite their potential, several barriers hinder widespread adoption-especially in developing countries. These include limited awareness among small-scale farmers, regulatory restrictions, infrastructural deficiencies, high initial costs and a shortage of skilled personnel. The review also highlights research gaps in long-term impact assessments, ethical considerations and integration with emerging technologies such as blockchain and the internet of things (IoT). To fully realize the transformative potential of drones in global agriculture, the article concludes by emphasizing the need for inclusive, context-specific solutions, enabling legislation and international collaboration.

References

  1. 1. Zhang C, Kovacs JM. The application of small unmanned aerial systems for precision agriculture: A review. Precision Agric. 2012;13:693–712. https://doi.org/10.1007/s11119-012-9274-5
  2. 2. Tsouros DC, Bibi S, Sarigiannidis PG. A review on UAV-based applications for precision agriculture. Information. 2019;10(11):349. https://doi.org/10.3390/info10110349
  3. 3. Topaj A, Mirschel W. Abnormal shapes of production function: Model interpretations. Comput Electron Agric. 2018;145:199–207. https://doi.org/10.1016/j.compag.2017.12.039
  4. 4. Bendig J, Bolten A, Bennertz S, Broscheit J, Eichfuss S, Bareth G. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens. 2014;6(11):10395–412. https://doi.org/10.3390/rs61110395
  5. 5. Cuaran J, Leon J. Crop monitoring using unmanned aerial vehicles: A review. Agric Rev. 2021;42(2):121–32. https://doi.org/10.18805/ag.R-180
  6. 6. dela Torre DMG, Gao J, Macinnis-Ng C. Remote sensing-based estimation of rice yields using various models: A critical review. Geo-Spatial Info Sci. 2021;24(4):580–603. https://doi.org/10.1080/10095020.2021.1936656
  7. 7. Maes WH, Steppe K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci. 2019;24(2):152–64. https://doi.org/10.1016/j.tplants.2018.11.007
  8. 8. Hunt Jr ER, Daughtry CST. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? Int J Remote Sens. 2018;39(15–16):5345–76. https://doi.org/10.1080/01431161.2017.1410300
  9. 9. Mogili UMR, Deepak B. Review on application of drone systems in precision agriculture. Procedia Comput Sci. 2018;133:502–509. https://doi.org/10.1016/j.procs.2018.07.063
  10. 10. Adão T, Hruška J, Pádua L, Bessa J, Peres E, Morais R, et al. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 2017;9(11):1110. https://doi.org/10.3390/rs9111110
  11. 11. Gano B, Bhadra S, Vilbig JM, Ahmed N, Sagan V, Shakoor N. Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review. Plant Phenome J. 2024;7(1):e20100. https://doi.org/10.1002/ppj2.20100
  12. 12. Acharya BS, Bhandari M, Bandini F, Pizarro A, Perks M, Joshi DR, et al. Unmanned aerial vehicles in hydrology and water management: Applications, challenges, and perspectives. Water Resour Res. 2021;57(11):e2021WR029925. https://doi.org/10.1029/2021WR029925
  13. 13. Peña JM, Torres-Sánchez J, de Castro AI, Kelly M, López-Granados F. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One. 2013;8(10):e77151. https://doi.org/10.1371/journal.pone.0077151
  14. 14. Tokekar P, Vander Hook J, Mulla D, Isler V. Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Trans Robotics. 2016;32(6):1498–511. https://doi.org/10.1109/TRO.2016.2603528
  15. 15. Balkrishna A, Pathak R, Kumar S, Arya V, Singh SK. A comprehensive analysis of the advances in Indian digital agricultural architecture. Smart Agric Technol. 2023;5:100318. https://doi.org/10.1016/j.atech.2023.100318
  16. 16. Omia E, Bae H, Park E, Kim MS, Baek I, Kabenge I, et al. Remote sensing in field crop monitoring: A comprehensive review of sensor systems, data analyses and recent advances. Remote Sens. 2023;15(2):354. https://doi.org/10.3390/rs15020354
  17. 17. Zualkernan I, Abuhani DA, Hussain MH, Khan J, ElMohandes M. Machine learning for precision agriculture using imagery from unmanned aerial vehicles (UAVS): A survey. Drones. 2023;7(6):382. https://doi.org/10.3390/drones7060382
  18. 18. Pathak H, Kumar G, Mohapatra SD, Gaikwad BB, Rane J. Use of drones in agriculture: Potentials, problems and policy needs. ICAR-National Institute of Abiotic Stress Management. 2020;300:4–15.
  19. 19. Sah B, Gupta R, Bani-Hani D. Analysis of barriers to implement drone logistics. Int J Logistics Res Appl. 2021;24(6):531–50. https://doi.org/10.1080/13675567.2020.1782862
  20. 20. Puppala H, Peddinti PRT, Tamvada JP, Ahuja J, Kim B. Barriers to the adoption of new technologies in rural areas: The case of unmanned aerial vehicles for precision agriculture in India. Technol Soc. 2023;74:102335. https://doi.org/10.1016/j.techsoc.2023.102335
  21. 21. Schmidt R, Schadow J, Eißfeldt H, Pecena Y. Insights on remote pilot competences and training needs of civil drone pilots. Transp Res Procedia. 2022;66:1–7. https://doi.org/10.1016/j.trpro.2022.12.001
  22. 22. Hoek Spaans R, Drumond B, van Daalen KR, Rorato Vitor AC, Derbyshire A, Da Silva A, et al. Ethical considerations related to drone use for environment and health research: A scoping review protocol. PLoS One. 2024;19(1):e0287270. https://doi.org/10.1371/journal.pone.0287270
  23. 23. Zuo A, Wheeler SA, Sun H. Flying over the farm: Understanding drone adoption by Australian irrigators. Precis Agric. 2021;22(6):1973–91. https://doi.org/10.1007/s11119-021-09821-y
  24. 24. Raj A, Sah B. Analyzing critical success factors for implementation of drones in the logistics sector using grey-DEMATEL based approach. Comput Ind Eng. 2019;138:106118. https://doi.org/10.1016/j.cie.2019.106118
  25. 25. Sharma M, Hema N. Comparison of agricultural drones and challenges in implementation: A Review. In: 2021 7th International Conference on Signal Processing and Communication (ICSC). IEEE; 2021. p. 26–30. https://doi.org/10.1109/ICSC53193.2021.9673491
  26. 26. Da Silveira F, Lermen FH, Amaral FG. An overview of agriculture 4.0 development: Systematic review of descriptions, technologies, barriers, advantages, and disadvantages. Comput Electron Agric. 2021;189:106405. https://doi.org/10.1016/j.compag.2021.106405
  27. 27. Rejeb A, Abdollahi A, Rejeb K, Treiblmaier H. Drones in agriculture: A review and bibliometric analysis. Comput Electron Agric. 2022;198:107017. https://doi.org/10.1016/j.compag.2022.107017
  28. 28. Freeman PK, Freeland RS. Agricultural UAVs in the US: Potential, policy, and hype. Remote Sens Appl. 2015;2:35–43. https://doi.org/10.1016/j.rsase.2015.10.002
  29. 29. Elijah O, Rahman TA, Orikumhi I, Leow CY, Hindia MHDN. An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet Things J. 2018;5(5):3758–73. https://doi.org/10.1109/JIOT.2018.2844296
  30. 30. Rao B, Gopi AG, Maione R. The societal impact of commercial drones. Technol Soc. 2016;45:83–90. https://doi.org/10.1016/j.techsoc.2016.02.009
  31. 31. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res. 2021;133:285–96. https://doi.org/10.1016/j.jbusres.2021.04.070
  32. 32. Jesson J, Lacey FM, Matheson L. Doing your literature review: Traditional and systematic techniques; 2011.
  33. 33. Ellegaard O, Wallin JA. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics. 2015;105:1809–31. https://doi.org/10.1007/s11192-015-1645-z
  34. 34. Gasparyan AY, Yessirkepov M, Duisenova A, Trukhachev VI, Kostyukova EI, Kitas GD. Researcher and author impact metrics: Variety, value, and context. J Korean Med Sci. 2018;33(18):e139. https://doi.org/10.3346/jkms.2018.33.e139
  35. 35. Ayaz S, Masood N. Comparison of researchers’ impact indices. PLoS One. 2020;15(5):e0233765. https://doi.org/10.1371/journal.pone.0233765
  36. 36. Hirsch JE. An index to quantify an individual’s scientific research output. Proc Nat Acad Sci. 2005;102(46):16569–72. https://doi.org/10.1073/pnas.0507655102
  37. 37. Egghe L. Theory and practise of the g-index. Scientometrics. 2006;69(1):131–52. https://doi.org/10.1007/s11192-006-0144-7
  38. 38. Merigó JM, Yang JB. A bibliometric analysis of operations research and management science. Omega (Westport). 2017;73:37–48. https://doi.org/10.1016/j.omega.2016.12.004
  39. 39. Liu Y, Zhang Y, Bai Y. Applications of UAV based remote sensing in precision agriculture: A review. Comput Electron Agric. 2020;170:105251. https://doi.org/10.1016/j.compag.2020.105251
  40. 40. Ayamga M, Tekinerdogan B, Kassahun A. Exploring challenges posed by drone regulations in African agriculture. Land. 2021;10(2):164. https://doi.org/10.3390/land10020164
  41. 41. Costa C, Antonucci F, Pallottino F, Aguzzi J, Sun D, Menesatti P. A review on agri technologies based on UAVs. Sensors. 2020;20(18):5176. https://doi.org/10.3390/s20185176
  42. 42. Du X, Wang X, Li M, Li C. UAV remote sensing and AI in agriculture. Remote Sens. 2022;14(5):1123. https://doi.org/10.3390/rs14051123
  43. 43. Figorilli S, Antonucci F, Costa C, Pallottino F, Raso L. Blockchain traceability using UAVs in wood supply chains. Sensors. 2019;19(3):683. https://doi.org/10.3390/s19030683
  44. 44. Jagarlapudi A, Yellapu A, Maddi S. UAV based crop monitoring using ML image processing. Agric Res J. 2023;60(1):11–18.
  45. 45. Kaivosoja J, Salo H, Rautiainen T. Review of remote sensing technologies for precision agriculture. Precis Agric. 2018;19(5):900–26. https://doi.org/10.1007/s11119 018 9587 y
  46. 46. Pallottino F, Costa C, Figorilli S, Menesatti P. UAVs for precision agriculture: Data acquisition and analysis. Agron Res. 2020;18(S1):234–44.
  47. 47. Pingale R, Jadhav V, Mane M. Role of UAVs in Indian agriculture: Opportunities and challenges. Int J Agric Sci. 2021;13(2):112–17.
  48. 48. Raj R, Kumar A, Sharma A. Drone applications in precision farming: Emerging trends. J Agric Technol. 2021;17(3):45–54.
  49. 49. Callon M, Courtial JP, Laville F. Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics. 1991;22:155–205. https://doi.org/10.1007/BF02019280
  50. 50. Zahawi RA. Restoration of tropical forests: The potential of forest restoration programs in Latin America. Biol Conserv. 2015;182:23-34. https://doi.org/10.1016/j.biocon.2015.03.031
  51. 51. Huuskonen J. Evaluation of the effects of climate change on farm yields using remote sensing data. Comput Electron Agric. 2018;155:23-31. https://doi.org/10.1016/j.compag.2018.08.039
  52. 52. Jakob S. Remote sensing technology for precision agriculture. Remote Sens. 2017;9(1):88. https://doi.org/10.3390/rs9010088
  53. 53. Gnädinger F. Evaluation of remote sensing data for the assessment of vegetation health. Remote Sens. 2017;9(6):544. https://doi.org/10.3390/rs9060544
  54. 54. Latif G. The impact of remote sensing on environmental monitoring. Plants. 2022;11(17):2230. https://doi.org/10.3390/plants11172230
  55. 55. Reedha R. Remote sensing-based methods for crop monitoring and early warning. Remote Sens. 2022;14(3):592. https://doi.org/10.3390/rs14030592
  56. 56. Almalki FA. A study on sustainability in agriculture and remote sensing technologies. Sustainability. 2021;13(11):5908. https://doi.org/10.3390/su13115908
  57. 57. Gallo I. Evaluation of advanced remote sensing methods in agriculture. Remote Sens. 2023;15(2):539. https://doi.org/10.3390/rs15020539
  58. 58. Mesas-Carrascosa FJ. Remote sensing applications for soil health monitoring. Remote Sens. 2015;7(10):12793. https://doi.org/10.3390/rs71012793
  59. 59. Näsi R. Remote sensing techniques for environmental monitoring in agriculture. Remote Sens. 2018;10(7):1082. https://doi.org/10.3390/rs10071082
  60. 60. Alkhammash R. Bibliometric, network, and thematic mapping analyses of metaphor and discourse in COVID-19 publications from 2020 to 2022. Front Psychol. 2023;13:1062943. https://doi.org/10.3389/fpsyg.2022.1062943
  61. 61. Murphy KR, Cleveland JN. Understanding performance appraisal: Social, organizational, and goal-based perspectives. Sage Publications; 1995.

Downloads

Download data is not yet available.