Cybersecurity Challenges in Digital Agriculture: A Hybrid AI-Based Framework

DOI

10.2991/978-94-6239-791-0_37How to use a DOI?

Keywords

Agriculture; Precision Agriculture; Artificial Intelligence; Smart Agriculture; Hybrid AI Models; AI Based Predictive Models; Machine Learning Models

Abstract

Digital agriculture uses AI, ML, IoT, and precision farming to revolutionize farming. These technologies help sustainable agricultural growth by improving crop monitoring, irrigation management, disease detection, yield prediction, and resource optimization. However, smart farming ecosystems’ growing dependency on linked digital systems has raised cybersecurity concerns about data privacy, unsecured IoT devices, unwanted access, and system stability. The biggest cybersecurity problems in digital agriculture and current advances in AI-enabled agricultural systems are examined in this systematic analysis of 2020–2026 research. The results show that AI models like CNNs, Random Forest, and XGBoost improve agricultural decision-making, predictive analytics, and operational efficiency. Scalability, interoperability, computational complexity, and data security challenges still plague large-scale deployment. A hybrid AI-based architecture incorporating safe data capture, blockchain-enabled data management, intelligent analytics, and decision-support systems improves digital agricultural cybersecurity and sustainability, according to the research. The framework promotes future agricultural growth with robust, secure, and efficient smart farming ecosystems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Susmita Suresh Pagare
AU  - Snehal Ashish Patil
AU  - Tarun Madan Kanade
AU  - Snehal Belkhode
PY  - 2026
DA  - 2026/10/09
TI  - Cybersecurity Challenges in Digital Agriculture: A Hybrid AI-Based Framework
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 495
EP  - 505
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_37
DO  - 10.2991/978-94-6239-791-0_37
ID  - Pagare2026
ER  -