CYBERSECURITY THREATS AND MITIGATION STRATEGIES IN AGRICULTURE 4.0 AND 5.0: CHALLENGES AND SOLUTIONS IN THE DIGITAL TRANSFORMATION OF AGRICULTURE

Authors

  • Bhagwant Singh Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab India
  • Sikander Singh Cheema Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab India.

Keywords:

Cybersecurity, IoT in agriculture, Blockchain, AI security, Cyber-threats, Digital Agriculture

Abstract

Agriculture's digital evolution through Agriculture 4.0 and 5.0 brings unprecedented technological advancements, notably through IoT, AI, and Blockchain integration, which boost productivity, precision, and sustainability. However, this rapid adoption of connected and intelligent systems also presents a wide range of cybersecurity vulnerabilities that threaten data integrity, operational continuity, and privacy. This review identifies and categorizes key cybersecurity threats in Agriculture 4.0 and 5.0, examining specific risks associated with IoT devices, data privacy, AI models, and Blockchain applications in agriculture. It further explores mitigation strategies such as device encryption, Blockchain security protocols, Explainable AI (XAI) for transparency, and secure data-sharing practices to counteract these risks. By analyzing the interplay between Blockchain and AI, this study highlights synergies that enhance security, transparency, and trust within digital agriculture systems. In discussing ongoing challenges, including economic constraints and scalability issues, this review emphasizes the need for interdisciplinary research and tailored cybersecurity frameworks to safeguard agriculture’s digital transformation. Ultimately, securing Agriculture 4.0 and 5.0 is essential for strengthening global food systems, economic resilience, and the long-term sustainability of the agriculture sector.

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References

Al-Bassam, M., Sonnino, A., Bano, S., Hrycyszyn, D., & Danezis,

G. (2018). Chainspace: A Sharded Smart Contracts Platform. 25th Annual Network and Distributed System Security Symposium, NDSS 2018, February. https://doi.org/10.14722/ndss.2018.23241

Demestichas, K., Peppes, N., & Alexakis, T. (2020). Survey on security threats in agricultural iot and smart farming. Sensors (Switzerland), 20(22), 1–17. https://doi.org/10.3390/s20226458

Dineva, K., & Atanasova, T. (2022). Cloud Data-Driven Intelligent Monitoring System for Interactive Smart Farming. Sensors, 22(17). https://doi.org/10.3390/s22176566

Fan, J., Li, Y., Yu, S., Gou, W., Guo, X., & Zhao, C. (2023).

Application of Internet of Things to Agriculture—The LQ-FieldPheno Platform: A High-Throughput Platform for Obtaining Crop Phenotypes in Field. Research, 6, 1–

https://doi.org/10.34133/research.0059

Fei, S., Hassan, M. A., Xiao, Y., Su, X., Chen, Z., Cheng, Q., Duan, F., Chen, R., & Ma, Y. (2023). UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precision Agriculture, 24(1), 187–

https://doi.org/10.1007/s11119-022-09938-8

Gazzola, P., Pavione, E., Barge, A., & Fassio, F. (2023). Using the Transparency of Supply Chain Powered by Blockchain to Improve Sustainability Relationships with Stakeholders in the Food Sector: The Case Study of Lavazza. Sustainability (Switzerland), 15(10). https://doi.org/10.3390/su15107884

Goldstein, A., Fink, L., & Ravid, G. (2022). A Cloud-Based Framework for Agricultural Data Integration: A Top- Down-Bottom-Up Approach. IEEE Access, 10, 88527–

https://doi.org/10.1109/ACCESS.2022.3198099

Hameed, H., Zafar, N. A., Alkhammash, E. H., & Hadjouni, M. (2022). Blockchain-Based Formal Model for Food Supply Chain Management System Using VDM-SL. Sustainability (Switzerland), 14(21). https://doi.org/10.3390/su142114202

Ibrahim, I. A., & Truby, J. M. (2023). FarmTech: Regulating the use of digital technologies in the agricultural sector. Food and Energy Security, 12(4), 1–15. https://doi.org/10.1002/fes3.483

Joshi, A., Pradhan, B., Gite, S., & Chakraborty, S. (2023). Remote- Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review. Remote Sensing, 15(8). https://doi.org/10.3390/rs15082014

Kurniawan, A., Ohsita, Y., & Murata, M. (2022). Experiments on Adversarial Examples for Deep Learning Model Using Multimodal Sensors. Sensors, 22(22). https://doi.org/10.3390/s22228642

Lahza, H., Naveen Kumar, K. R., Sreenivasa, B. R., Shawly, T., Alsheikhy, A. A., Hiremath, A. K., & Lahza, H. F. M. (2023). Optimization of Crop Recommendations Using Novel Machine Learning Techniques. Sustainability (Switzerland), 15(11), 1–18. https://doi.org/10.3390/su15118836

Lin, N., Wang, X., Zhang, Y., Hu, X., & Ruan, J. (2020). Fertigation management for sustainable precision agriculture based on Internet of Things. Journal of Cleaner Production,

https://doi.org/10.1016/j.jclepro.2020.124119

Lo, V. S. Y., & Pachamanova, D. A. (2023). From Meaningful Data Science to Impactful Decisions: The Importance of Being Causally Prescriptive. Data Science Journal, 22(1), 1–18.

https://doi.org/10.5334/dsj-2023-008

Ma, X. (2023). Smart Agriculture and Rural Revitalization and Development Based on the Internet of Things under the Background of Big Data. Sustainability (Switzerland),

(4). https://doi.org/10.3390/su15043352

MacPherson, J., Voglhuber-Slavinsky, A., Olbrisch, M., Schöbel, P., Dönitz, E., Mouratiadou, I., & Helming, K. (2022). Future agricultural systems and the role of digitalization for achieving sustainability goals. A review. Agronomy for Sustainable Development, 42(4). https://doi.org/10.1007/s13593-022-00792-6

Maraveas, C., Rajarajan, M., Arvanitis, K. G., & Vatsanidou, A. (2024). Cybersecurity threats and mitigation measures in agriculture 4.0 and 5.0. Smart Agricultural Technology, 9(September).

https://doi.org/10.1016/j.atech.2024.100616

Martin Otieno. (2023). An extensive survey of smart agriculture technologies: Current security posture. World Journal of Advanced Research and Reviews, 18(3), 1207–1231. https://doi.org/10.30574/wjarr.2023.18.3.1241

Méndez-Guzmán, H. A., Padilla-Medina, J. A., Martínez-Nolasco, C., Martinez-Nolasco, J. J., Barranco-Gutiérrez, A. I., Contreras-Medina, L. M., & Leon-Rodriguez, M. (2022). IoT-Based Monitoring System Applied to Aeroponics Greenhouse. Sensors, 22(15). https://doi.org/10.3390/s22155646

Mesías-Ruiz, G. A., Pérez-Ortiz, M., Dorado, J., de Castro, A. I., & Peña, J. M. (2023). Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. Frontiers in Plant Science, 14(March), 1–22. https://doi.org/10.3389/fpls.2023.1143326

Nurse, J. R. C. (2021). Cybersecurity Awareness. Encyclopedia of Cryptography, Security and Privacy, 1–4. https://doi.org/10.1007/978-3-642-27739-9_1596-1

Oliveira, R. C. de, & Silva, R. D. de S. e. (2023). Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends. Applied Sciences (Switzerland), 13(13). https://doi.org/10.3390/app13137405

Raturi, A., Thompson, J. J., Ackroyd, V., Chase, C. A., Davis, B. W., Myers, R., Poncet, A., Ramos-Giraldo, P., Reberg- Horton, C., Rejesus, R., Robertson, A., Ruark, M. D., Seehaver-Eagen, S., & Mirsky, S. (2022). Cultivating trust in technology-mediated sustainable agricultural research. Agronomy Journal, 114(5), 2669–2680. https://doi.org/10.1002/agj2.20974

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Published

2024-12-29

How to Cite

Bhagwant Singh, & Sikander Singh Cheema. (2024). CYBERSECURITY THREATS AND MITIGATION STRATEGIES IN AGRICULTURE 4.0 AND 5.0: CHALLENGES AND SOLUTIONS IN THE DIGITAL TRANSFORMATION OF AGRICULTURE. Journal Punjab Academy of Sciences, 24, 15–32. Retrieved from https://jpas.in/index.php/home/article/view/96