A Systematic Literature Review on IoT Integrated Agriculture: Study Illustrated Security and Productivity

The integration of Internet of Things (IoT) technologies in agriculture has created unprecedented opportunities for enhancing both security and productivity, while simultaneously introducing complex challenges that span cyber, physical, and operational domains. This systematic literature review examines the bidirectional relationship between security measures and productivity outcomes in IoT-enabled agriculture. Following the customize PRISMA guidelines and systematic review methodology [1], this study synthesizes findings from 32 peer-reviewed articles published between 2018 and 2025, sourced from major academic databases with an initial population of 401 studies. The review addresses three research questions. Key findings reveal that security frameworks achieve 92-99% effectiveness in intrusion detection, authentication, and data integrity, while productivity gains include 20-35% yield improvement, 30-50% water savings, and 15-25% reduction in harvest time. The analysis demonstrates a strong positive correlation between security and productivity: secure IoT systems enhance operational continuity, decision-making accuracy, and stakeholder trust, directly contributing to productivity gains. However, significant research gaps persist in integrated physical-cyber security frameworks, real-world validation, and standardized metrics for security-productivity correlation. This review provides a comprehensive foundation for researchers, practitioners, and policymakers working toward resilient, secure, and productive IoT-enabled agricultural systems aligned with Sustainable Development Goal 2 (Zero Hunger).

Internet of Things, Smart Agriculture, Agricultural Security

1. Carrera-Rivera, W. Ochoa, F. Larrinaga, and G. Lasa, "How-to conduct a systematic literature review: A quick guide for computer science research," MethodsX, vol. 9, p. 101895, 2022. [Google Scholar] [Crossref]

2. M. Farooq, S. Riaz, A. Abid, K. Abid, and M. Naeem, "A survey on the role of IoT in agriculture for the implementation of smart farming," IEEE Access, vol. 7, pp. 156237–156271, 2019. [Google Scholar] [Crossref]

3. M. Dhanaraju, P. Chenniappan, K. Ramalingam, S. Pazhanivelan, and R. Kaliaperumal, "Smart farming: Internet of Things (IoT)-based sustainable agriculture," Agriculture, vol. 12, no. 10, p. 1745, 2022. [Google Scholar] [Crossref]

4. S. O. Oruma, S. Misra, and L. Fernandez-Sanz, "Agriculture 4.0: An implementation framework for food security attainment in Nigeria's post-Covid-19 era," IEEE Access, vol. 9, pp. 83592–83627, 2021. [Google Scholar] [Crossref]

5. Morchid, R. Alami, A. Raezah, and Y. Sabbar, "Applications of internet of things (IoT) and sensors technology to increase food security and agricultural sustainability: Benefits and challenges," Ain Shams Engineering Journal, vol. 15, no. 3, p. 102509, 2023. [Google Scholar] [Crossref]

6. H. Shahab, M. Naeem, M. Iqbal, M. Aqeel, and S. S. Ullah, "IoT-driven smart agricultural technology for real-time soil and crop optimization," Smart Agricultural Technology, vol. 10, p. 100847, 2025. [Google Scholar] [Crossref]

7. Vangala, A. Das, V. Chamola, V. Korotaev, and J. Rodrigues, "Security in IoT-enabled smart agriculture: architecture, security solutions and challenges," Cluster Computing, vol. 26, pp. 879–902, 2022. [Google Scholar] [Crossref]

8. M. Gupta, M. Abdelsalam, S. Khorsandroo, and S. Mittal, "Security and privacy in smart farming: Challenges and opportunities," IEEE Access, vol. 8, pp. 34564–34584, 2020. [Google Scholar] [Crossref]

9. K. Demestichas, N. Peppes, and T. Alexakis, "Survey on security threats in agricultural IoT and smart farming," Sensors, vol. 20, no. 22, p. 6458, 2020. [Google Scholar] [Crossref]

10. Riaz et al., "Applying adaptive security techniques for risk analysis of Internet of Things (IoT)-based smart agriculture," Sustainability, vol. 14, no. 17, p. 10964, 2022. [Google Scholar] [Crossref]

11. X. Yang et al., "A survey on smart agriculture: Development modes, technologies, and security and privacy challenges," IEEE/CAA Journal of Automatica Sinica, vol. 8, pp. 273–302, 2021. [Google Scholar] [Crossref]

12. El-Ghamry, A. Darwish, and A. E. Hassanien, "An optimized CNN-based intrusion detection system for reducing risks in smart farming," Internet of Things, vol. 22, p. 100709, 2023. [Google Scholar] [Crossref]

13. N. Shingari and B. Mago, "A framework for application-centric Internet of Things authentication," Results in Engineering, vol. 22, p. 102109, 2024. [Google Scholar] [Crossref]

14. S. Itoo, A. A. Khan, M. Ahmad, and M. J. Idrisi, "A secure and privacy-preserving lightweight authentication and key exchange algorithm for smart agriculture monitoring system," IEEE Access, vol. 11, pp. 56875–56890, 2023. [Google Scholar] [Crossref]

15. Vangala, A. K. Das, A. Mitra, S. K. Das, and Y. Park, "Blockchain-enabled authenticated key agreement scheme for mobile vehicles-assisted precision agricultural IoT networks," IEEE Transactions on Information Forensics and Security, vol. 18, pp. 904–919, 2023. [Google Scholar] [Crossref]

16. Abunadi, A. Rehman, K. Haseeb, L. Parra, and J. Lloret, "Traffic-aware secured cooperative framework for IoT-based smart monitoring in precision agriculture," Sensors, vol. 22, no. 17, p. 6676, 2022. [Google Scholar] [Crossref]

17. Ali, W. Bukhari, M. Adnan, M. Kashif, A. Danish, and A. Sikander, "Security and privacy in IoT-based smart farming: A review," Multimedia Tools and Applications, vol. 84, pp. 15971–16031, 2024. [Google Scholar] [Crossref]

18. Morchid et al., "High-technology agriculture system to enhance food security: A concept of smart irrigation system using Internet of Things and cloud computing," Journal of the Saudi Society of Agricultural Sciences, 2024. [Google Scholar] [Crossref]

19. Chandrashekar. M. et al, "IoT-integrated farm security system with real-time alerts and intrusion detection," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 3, 2025. [Google Scholar] [Crossref]

20. Morchid, Z. Oughannou, R. Alami, H. Qjidaa, M. Jamil, and H. Khalid, "Integrated Internet of Things (IoT) solutions for early fire detection in smart agriculture," Results in Engineering, vol. 22, p. 103392, 2024. [Google Scholar] [Crossref]

21. P. Singh and R. Krishnamurthi, "IoT-based real-time object detection system for crop protection and agriculture field security," Journal of Real-Time Image Processing, vol. 21, 2024. [Google Scholar] [Crossref]

22. Y. Adhitya, G. S. Mulyani, M. Koppen, and J.-S. Leu, "IoT and deep learning-based farmer safety system," Sensors, vol. 23, no. 6, p. 2951, 2023. [Google Scholar] [Crossref]

23. Murugananthan U et al., "Secure and transparent smart agriculture: Integrating blockchain technology with IoT for enhanced data integrity and efficiency in agricultural system," in 2025 International Conference on Emerging Smart Computing and Informatics (ESCI), 2025, pp. 1–6. [Google Scholar] [Crossref]

24. N. Sayem et al., "IoT-based smart protection system to address agro-farm security challenges in Bangladesh," Smart Agricultural Technology, vol. 6, p. 100358, 2023. [Google Scholar] [Crossref]

25. Dahane, R. Benameur, B. Kechar, and A. Benyamina, "An IoT based smart farming system using machine learning," in 2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020, pp. 1–6. [Google Scholar] [Crossref]

26. E. Palomar-Cosín and M. García-Valls, "Flexible IoT agriculture systems for irrigation control based on software services," Sensors, vol. 22, no. 24, p. 9999, 2022. [Google Scholar] [Crossref]

27. M. Syari, U. Rahardja, T. Wellem, H. Purnomo, and R. Buaton, "IoT enabled smart farming system for optimizing crop management using sensors and machine learning," in 2025 4th International Conference on Creative Communication and Innovative Technology (ICCIT), 2025, pp. 1–7. [Google Scholar] [Crossref]

28. Ali, T. Hussain, N. Tantashutikun, N. Hussain, and G. Cocetta, "Application of smart techniques, Internet of Things and data mining for resource use efficient and sustainable crop production," Agriculture, vol. 13, no. 2, p. 397, 2023. [Google Scholar] [Crossref]

29. V. Kumar, K. Sharma, N. Kedam, A. Patel, T. Kate, and U. Rathnayake, "A comprehensive review on smart and sustainable agriculture using IoT technologies," Smart Agricultural Technology, vol. 8, p. 100487, 2024. [Google Scholar] [Crossref]

30. Naseer, M. Shmoon, T. Shakeel, S. Rehman, A. Ahmad, and V. Gruhn, "A systematic literature review of the IoT in agriculture—global adoption, innovations, security, and privacy challenges," IEEE Access, vol. 12, pp. 60986–61021, 2024. [Google Scholar] [Crossref]

31. M. Rahaman, C. Lin, P. Pappachan, B. Gupta, and C. Hsu, "Privacy-centric AI and IoT solutions for smart rural farm monitoring and control," Sensors, vol. 24, no. 13, p. 4157, 2024. [Google Scholar] [Crossref]

32. V. Viswanatha, A. Ramachandra, P. Hegde, M. Reddy, V. Hegde, and V. Sabhahit, "Implementation of smart security system in agriculture fields using embedded machine learning," in 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), 2023, pp. 1–6. [Google Scholar] [Crossref]

33. Fathy and H. Ali, "A secure IoT-based irrigation system for precision agriculture using the expeditious cipher," Sensors, vol. 23, no. 4, p. 2091, 2023. [Google Scholar] [Crossref]

34. R. M. Groves et al., Survey Methodology, 2nd ed. New York: John Wiley & Sons, 2009. [Google Scholar] [Crossref]

35. J. P. T. Higgins et al., Eds., Cochrane Handbook for Systematic Reviews of Interventions, 2nd ed. New York: John Wiley & Sons, 2019. [Google Scholar] [Crossref]

36. W. G. Cochran, Sampling Techniques, 3rd ed. New York: John Wiley & Sons, 1977. [Google Scholar] [Crossref]

37. S. K. Thompson, Sampling, 3rd ed. New York: John Wiley & Sons, 2012. [Google Scholar] [Crossref]

38. B. Kitchenham and S. Charters, "Guidelines for performing systematic literature reviews in software engineering," Keele University, Keele, UK, Tech. Rep. EBSE-2007-01, 2007. [Google Scholar] [Crossref]

39. M. Petticrew and H. Roberts, Systematic Reviews in the Social Sciences: A Practical Guide. Oxford, UK: Blackwell Publishing, 2006. [Google Scholar] [Crossref]

40. J. Webster and R. T. Watson, "Analyzing the past to prepare for the future: Writing a literature review," MIS Quarterly, vol. 26, no. 2, pp. xiii–xxiii, 2002. [Google Scholar] [Crossref]

41. M. Borenstein, L. V. Hedges, J. P. T. Higgins, and H. R. Rothstein, Introduction to Meta-Analysis, 2nd ed. New York: John Wiley & Sons, 2021. [Google Scholar] [Crossref]

42. J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates, 1988. [Google Scholar] [Crossref]

43. G. Guest, A. Bunce, and L. Johnson, "How many interviews are enough? An experiment with data saturation and variability," Field Methods, vol. 18, no. 1, pp. 59–82, 2006. [Google Scholar] [Crossref]

44. L. Kish, Survey Sampling. New York: John Wiley & Sons, 1965. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet