Skip to main navigation menu Skip to main content Skip to site footer

Review Articles

Vol. 12 No. sp4 (2025): Recent Advances in Agriculture by Young Minds - III

Cyber-Secured Agriculture (Part 1): Big Data genesis

DOI
https://doi.org/10.14719/pst.10661
Submitted
15 July 2025
Published
22-12-2025

Abstract

Big data, Blockchain, Robotics and Artificial Intelligence (AI) stand as the multifaceted technological titans of our era, rapidly transforming every sector and poised to redefine the global agricultural domain. To explore this paradigm, we inaugurate a multi-part review series on cyber-secured agriculture. The first installment, Big Data genesis, examines how agriculture’s raw data has evolved into actionable agri-intelligence. The central hypothesis of this paper is that the systematic harnessing and analysis of agricultural Big Data is no longer optional but essential for ensuring productivity, sustainability and resilience in the face of mounting global challenges. To evaluate this hypothesis, we conduct a structured review of peer-reviewed literature, patents and technical databases. This study demonstrates that agricultural Big Data is not only propelling advances in precision farming but also forging overlooked connections with economics, policy and rural livelihoods. Whereas earlier reviews have focused narrowly on the advantages of Big Data in precision agriculture or on isolated technical aspects, this work advances a process-centric perspective, introducing a prototype framework that traces the full data continuum of agriculture. Concluding with forward-looking perspectives, this paper sets the stage for Part 2: Blockchain Nexus, which will explore how the agricultural data discussed in Part 1 can be secured through distributed ledger technologies. Future installments, Part 3: Robotic Lumina, Part 4: AI Rebooted and Part 5: Crypto Epoch, will extend the horizon by examining automation frontiers and intelligent reconfigurations of agriculture. Together, this evolving series offers a visionary blueprint for a secured and digitally empowered agricultural future.

References

  1. 1. Porter TM. Trust in numbers: The pursuit of objectivity in science and public life. Princeton (NJ): Princeton University Press; 2020.
  2. 2. Kitchin R. The data revolution: Big data, open data, data infrastructures and their consequences. London: Sage; 2014.
  3. 3. Mayer-Schönberger V, Cukier K. Big data: A revolution that will transform how we live, work and think. Boston: Houghton Mifflin Harcourt; 2014.
  4. 4. Borgman CL. Big data, little data, no data: Scholarship in the networked world. Cambridge (MA): MIT Press; 2017.
  5. 5. Edge Delta. 11 Insightful Statistics on Data Market Size and Forecast. Seattle (WA): Edge Delta; 2024.
  6. 6. Kitchin R. Big data. In: The data revolution: big data, open data, data infrastructures & their consequences. London: SAGE Publications Ltd; 2014. p. 67–79. https://doi.org/10.4135/9781473909472
  7. 7. Poger D, Yen L, Braet F. Big data in contemporary electron microscopy: challenges and opportunities in data transfer, compute and management. Histochem Cell Biol. 2023;160(3):169–92. https://doi.org/10.1007/s00418-023-02191-8
  8. 8. Assunção MD, Calheiros RN, Bianchi S, Netto MAS, Buyya R. Big data computing and clouds: trends and future directions. J Parallel Distrib Comput. 2015;79–80:3–15. https://doi.org/10.1016/j.jpdc.2014.08.003
  9. 9. Hilbert M. Big data for development: a review of promises and challenges. Dev Policy Rev. 2016;34(1):135–74. https://doi.org/10.1111/dpr.12142
  10. 10. Srinivas K, Rao DN. A privacy based deep learning algorithm for big data analytics. Informatica. 2025;49(2):193–204.
  11. 11. Wang Y, Li Y, Zhang J. Is intelligence the answer to deal with the 5 V’s of telemetry data? IEEE Access. 2023;11:12345–57. https://doi.org/10.1109/ACCESS.2023.3285678
  12. 12. Zhang Y, Liu X, Chen H. Knowledge graph for solubility big data: construction and applications. Wiley Interdiscip Rev Data Min Knowl Discov. 2024;14(6):e1570. https://doi.org/10.1002/widm.1570
  13. 13. Kaur M, Kaur P. Big data characteristics, architecture, technologies and applications. J Comput Sci. 2020;16(6):817–24.
  14. 14. Khan R, Hoque ASML. Fifty-six big data V’s characteristics and proposed strategies to overcome security and privacy challenges (BD2). J Comput Commun. 2020;8(8):1–15.
  15. 15. Rani S, Kumar S. Big data and its applications in smart real estate and the disaster management life cycle: a systematic analysis. J Data Intell. 2020;4(2):4.
  16. 16. Al Nuaimi E, Al Neyadi H, Mohamed N, Al-Jaroodi J. Applications of big data to smart cities. J Internet Serv Appl. 2015;6:25. https://doi.org/10.1186/s13174-015-0041-5
  17. 17. Kitchin R. Big data, new epistemologies and paradigm shifts. Big Data Soc. 2014;1(1):1–12. https://doi.org/10.1177/2053951714528481
  18. 18. Hussain F, Nauman M, Alghuried A, Alhudhaif A, Akhtar N. Leveraging big data analytics for enhanced clinical decision-making in healthcare. IEEE Access. 2023;11:127817–36. https://doi.org/10.1109/access.2023.3332030
  19. 19. Archenaa J, Anita EM. A survey of big data analytics in healthcare and government. Procedia Comput Sci. 2015;50:408–13. https://doi.org/10.1016/j.procs.2015.04.021
  20. 20. Bendre MR, Thool VR. Analytics, challenges and applications in big data environment: a survey. J Manag Anal. 2016;3:206–39. https://doi.org/10.1080/23270012.2016.1186578
  21. 21. Thayyib PV, Mamilla R, Khan M, Fatima H, Asim M, Anwar I, et al. State-of-the-art of artificial intelligence and big data analytics reviews in five different domains: a bibliometric summary. Sustainability. 2023;15(5):4026. https://doi.org/10.3390/su15054026
  22. 22. Berthelé E. Using big data in insurance. Big Data Insur Comp. 2018;1:131–61. https://doi.org/10.1002/9781119489368.ch5
  23. 23. Lee I, Mangalaraj G. Big data analytics in supply chain management: a systematic literature review and research directions. Big Data Cogn Comput. 2022;6(1):17. https://doi.org/10.3390/bdcc6010017
  24. 24. Razzak I, Eklund P, Xu G. Improving healthcare outcomes using multimedia big data analytics. Neural Comput Appl. 2022;34(17):15095–7. https://doi.org/10.1007/s00521-022-07250-7
  25. 25. Bibri SE. Advancing sustainable urbanism processes: the key practical and analytical applications of big data for urban systems and domains. In: Big data science and analytics for smart sustainable urbanism: unprecedented paradigmatic shifts and practical advancements. Cham: Springer; 2019. p. 221–52. https://doi.org/10.1007/978-3-030-02312-6_8
  26. 26. Jain H, Jain R. Big data in weather forecasting: applications and challenges. In: 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC); 2017. p. 138–42. https://doi.org/10.1109/ICBDACI.2017.8070815
  27. 27. Akash TR, Islam MS, Sourav MSA. Enhancing business security through fraud detection in financial transactions. Glob J Eng Technol Adv. 2024;21(02):79–87.
  28. 28. Osinga SA, Paudel D, Mouzakitis SA, Athanasiadis IN. Big data in agriculture: between opportunity and solution. Agric Syst. 2022;195:103298. https://doi.org/10.1016/j.agsy.2021.103298
  29. 29. Patil BD, Gupta S, Sheikh AI, Lalitha SSKDP, Raj KDGB. IoT and big data integration for real-time agricultural monitoring. J Adv Zool. 2023;44:3079–89.
  30. 30. Rajeswari SKRA, Suthendran K, Rajakumar K. A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics. In: 2017 International Conference on Intelligent Computing and Control (I2C2); 2017. p. 1–5. https://doi.org/10.1109/I2C2.2017.8321916
  31. 31. Zhang C, Liu Z. Application of big data technology in agricultural Internet of Things. Int J Distrib Sens Netw. 2019;15(10):1550147719881610. https://doi.org/10.1177/1550147719881610
  32. 32. Alahmad T, Neményi M, Nyéki A. Applying IoT sensors and big data to improve precision crop production: a review. Agronomy. 2023;13(10):2603. https://doi.org/10.3390/agronomy13102603
  33. 33. Elijah O, Rahman TA, Orikumhi I, Leow CY, Hindia MN. An overview of Internet of Things (IoT) and data analytics in agriculture: benefits and challenges. IEEE Internet Things J. 2018;5(5):3758–77. https://doi.org/10.1109/JIOT.2018.2844296
  34. 34. Mishra S, Misra A. Structured and unstructured big data analytics. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC); 2017; Mysuru, India. Piscataway (NJ): IEEE; 2017. p. 740–6. https://doi.org/10.1109/CTCEEC.2017.8455075
  35. 35. Akter S, Wamba SF. Big data analytics in e-commerce: a systematic review and agenda for future research. Electron Mark. 2016;26(2):173–94. https://doi.org/10.1007/s12525-016-0219-0
  36. 36. Cravero A, Pardo S, Galeas P, López Fenner J, Caniupán M. Data type and data sources for agricultural big data and machine learning. Sustainability. 2022;14(23):16131. https://doi.org/10.3390/su142316131
  37. 37. Uyar H, Karvelas I, Rizou S, Fountas S. Data value creation in agriculture: A review. Comput Electron Agric. 2024;227:109602. https://doi.org/10.1016/j.compag.2024.109602
  38. 38. Sadiku MN, Ashaolu TJ, Musa SM. Big data in agriculture. Int J Sci Adv. 2020;1(1):44–8.
  39. 39. Sadeghi ME. Big data based on IoT in the agriculture industry: developments, opportunities and challenges ahead. J Data Anal. 2022;1(1):25–32.
  40. 40. Shvets Y, Morkovkin D, Chupin A, Ostroumov V, Shmanev S. Big data and analytics for crop yield forecasting: empirical research and development prospects. E3S Web Conf. 2024;480:03023. https://doi.org/10.1051/e3sconf/202448003023
  41. 41. Wolfert S, Ge L, Verdouw C, Bogaardt MJ. Big data in smart farming - a review. Agric Syst. 2017;153:69–80. https://doi.org/10.1016/j.agsy.2017.01.023
  42. 42. Kamilaris A, Kartakoullis A, Prenafeta-Boldú FX. A review on the practice of big data analysis in agriculture. Comput Electron Agric. 2017;143:23–37. https://doi.org/10.1016/j.compag.2017.09.037
  43. 43. Palniladevi P, Sabapathi T, Kanth DA, Kumar BP. IoT based smart agriculture monitoring system using renewable energy sources. In: 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN); 2023. p. 1–6. https://doi.org/10.1109/ViTECoN58111.2023.10157272
  44. 44. Nieman B, Johnson CS, Pearce M, Marcrum T, Thorne MC, Ashby C, et al. Through the soil long range wireless power transfer for agricultural IoT networks. IEEE Trans Ind Electron. 2023;71(2):2090–9. https://doi.org/10.1109/TIE.2023.3266400
  45. 45. Lemus-Prieto F, Bermejo Martín JF, Gónzalez-Sánchez JL, Moreno Sánchez E. CultivData: application of IoT to the cultivation of agricultural data. IoT. 2021;2(4):564–89. https://doi.org/10.3390/iot2040029
  46. 46. Duguma AL, Bai X. Contribution of Internet of Things (IoT) in improving agricultural systems. Int J Environ Sci Technol. 2024;21(2):2195–208. https://doi.org/10.1007/s13762-023-05446-y
  47. 47. Makondo N, Kobo HI, Mathonsi TE, Mamushiane L. A review on edge computing in 5G-enabled IoT for agricultural applications: opportunities and challenges. In: 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET); 2023. p. 1–6. https://doi.org/10.1109/ICECET58911.2023.10389565
  48. 48. Moore EK, Kriesberg A, Schroeder S, Geil K, Haugen I, Barford C, et al. Agricultural data management and sharing: best practices and case study. Agron J. 2022;114(5):2624–34. https://doi.org/10.1002/agj2.21136
  49. 49. Tripathi S, Srinivas VV, Nanjundiah RS. Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol. 2006;330(3):621–30. https://doi.org/10.1016/j.jhydrol.2006.04.030
  50. 50. Urtubia A, Perez-Correa JR, Soto A, Pszczolkowski P. Using data mining techniques to predict industrial wine problem fermentations. Food Control. 2007;18(12):1512–7. https://doi.org/10.1016/j.foodcont.2006.11.007
  51. 51. Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H. A crop phenology detection method using time-series MODIS data. Remote Sens Environ. 2005;96(3–4):366–74. https://doi.org/10.1016/j.rse.2005.03.008
  52. 52. Armstrong L, Diepeveen D, Maddern R. The application of data mining techniques to characterize agricultural soil profiles. In: Proceedings of the 16th Australian Joint Conference on Artificial Intelligence; 2003. p. 1037–48.
  53. 53. Gutiérrez PA, Hervás-Martínez C, Carbonero-Ruz M. Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Comput Electron Agric. 2008;64(2):293–306. https://doi.org/10.1016/j.compag.2008.06.004
  54. 54. Marcot BG, Holthausen RS, Raphael MG, Rowland MM, Wisdom MJ. Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. For Ecol Manag. 2001;153(1–3):29–42. https://doi.org/10.1016/S0378-1127(01)00452-2
  55. 55. Wardlow BD, Egbert SL, Kastens JH. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sens Environ. 2007;108(3):290–310. https://doi.org/10.1016/j.rse.2006.11.021
  56. 56. Lucas MT, Chhajed D. Applications of location analysis in agriculture: a survey. J Oper Res Soc. 2004;55(6):561–78. https://doi.org/10.1057/palgrave.jors.2601704
  57. 57. Meyer GE, Neto JC, Jones DD, Hindman TW. Intensified fuzzy clusters for classifying plant, soil and residue regions of interest from color images. Comput Electron Agric. 2004;42(3):161–80. https://doi.org/10.1016/j.compag.2003.12.001
  58. 58. Frelat R, Lopez-Ridaura S, Giller KE, Herrero M, Douxchamps S. Drivers of household food availability in sub-Saharan Africa based on big data from small farms. Proc Natl Acad Sci U S A. 2016;113(2):458–63. https://doi.org/10.1073/pnas.1518384112
  59. 59. Jozwiaka A, Milkovics M, Lakne Z. A network-science support system for food chain safety: a case from Hungarian cattle production. Int Food Agribus Manag Rev. 2016;19(A):1–14. https://doi.org/10.22434/IFAMR2016.0014
  60. 60. Pierna JA, Baeten V, Dardenne P, Biston R, Lognay G. Combination of support vector machines and near-infrared imaging spectroscopy for detecting meat and bone meal in compound feeds. J Chemometr. 2004;18(7–8):341–9. https://doi.org/10.1002/cem.877
  61. 61. Delgado JA, Short NM Jr, Roberts DP, Vandenberg B. Big data analysis for sustainable agriculture on a geospatial cloud framework. Front Sustain Food Syst. 2019;3:54. https://doi.org/10.3389/fsufs.2019.00054
  62. 62. Mohammed Abdelmotaleb MY, Shrestha A, Rabie Ahmed SS. Digital twin in power system research and development: principle, scope and challenges. 2023. https://doi.org/10.1016/j.enrev.2023.100039
  63. 63. Stefko R, Michalikova KF, Strakova J, Novak A. Digital twin-based virtual factory and cyber-physical production systems… for industrial metaverse. Equilibrium. 2025;20(1). https://doi.org/10.24136/eq.3557
  64. 64. Chatterjee S, Kliestik T, Rowland Z, Bugaj M. Immersive collaborative business process and extended-reality-driven industrial metaverse technologies for economic value co-creation in 3D digital twin factories. Oeconomia Copernicana. 2025;16(1). https://doi.org/10.24136/oc.3596
  65. 65. Challoumis C. The dawn of artificial intelligence. In: XIX International Scientific Conference. London; 2024. p. 169–205.
  66. 66. Yange TS, Egbunu CO, Rufai MA, Onyekwere O, Abdulrahman AA, Abdulkadri I. Using prescriptive analytics for the determination of optimal crop yield. Int J Data Sci Anal. 2020;6(3):72–82.
  67. 67. Suguna R, Rani RU. Descriptive and predictive analytics of agricultural data using machine learning algorithms. In: Smart Agriculture: Emerging Pedagogies of Deep Learning, Machine Learning and Internet of Things. Boca Raton: CRC Press; 2021. p. 20–39.
  68. 68. Marong M, Husin NA, Zolkepli M, Affendey LS. Data-driven rice yield predictions and prescriptive analytics for sustainable agriculture in Malaysia. Int J Adv Comput Sci Appl. 2024;15(3):1–9.
  69. 69. Feuerriegel S, Hartmann J, Janiesch C, Zschech P. Generative AI. Bus Inf Syst Eng. 2024;66(1):111–26. https://doi.org/10.1007/s12599-023-00834-7
  70. 70. Hassani H, Silva ES. Predictions from generative artificial intelligence models: towards a new benchmark in forecasting practice. Information. 2024;15(6):291. https://doi.org/10.3390/info15060291
  71. 71. Şahin O, Karayel D. Generative artificial intelligence in business: a systematic review on the threshold of transformation. J Smart Syst Res. 2024;5(2):156–75. https://doi.org/10.58769/joinssr.1597110
  72. 72. Kliestik T, Kral P, Bugaj M, Durana P. Generative artificial intelligence of things systems and multisensory immersive extended-reality technologies in digital twin industrial metaverse. Equilibrium. 2024;19(2). https://doi.org/10.24136/eq.3108
  73. 73. Zvarikova K, Gajanova L, Horak J. Exploring CSR performance as a proxy for competitive advantage across sectors in Central European countries. Oeconomia Copernicana. 2024;15(3). https://doi.org/10.24136/oc.3247
  74. 74. Pavolova H, Bakalár T, Kyšeľa K, Klimek M, Hajduova Z, Zawada M. The analysis of investment into industries based on portfolio managers. Acta Montan Slovaca. 2021;26(1). https://doi.org/10.46544/ams.v26i1.14
  75. 75. Weersink A, Fraser E, Pannell D, Duncan E, Rotz S. Opportunities and challenges for big data in agricultural and environmental analysis. Annu Rev Resour Econ. 2018;10(1):19–37. https://doi.org/10.1146/annurev-resource-100517-023156
  76. 76. Coble KH, Mishra AK, Ferrell S, Griffin T. Big data in agriculture: a challenge for the future. Appl Econ Perspect Policy. 2018;40(1):79–96. https://doi.org/10.1093/aepp/ppx056

Downloads

Download data is not yet available.