Agriculture 5.0, marked by advanced technology and intensified human-machine collaboration, addresses significant challenges in traditional farming, such as labor shortages, declining productivity, climate change impacts, and gender disparities. This study assesses the effectiveness of probabilistic machine learning methods, with a specific focus on Bayesian networks (BN), collaborative filtering (CF), and fuzzy cognitive map (FCM) techniques, in enhancing cybersecurity risk analysis and management in Agriculture 5.0. It also explores unique cybersecurity threats within Agriculture 5.0. Using a systematic literature review (SLR), and leveraging historical data, case studies, experimental datasets, probabilistic machine learning algorithms, experiments, expert insights, and data analysis tools, the study evaluates the effectiveness of these techniques in improving cybersecurity risk analysis in Agriculture 5.0. BN, CF, and FCM were found effective in enhancing the cybersecurity of Agriculture 5.0. This research enhances our understanding of how probabilistic machine learning can bolster cybersecurity within Agriculture 5.0. The study's insights will be valuable to industry stakeholders, policymakers, and cybersecurity professionals, aiding the protection of agriculture's digital transformation amid increasing technological complexity and cyber threats, and setting the stage for future investigations into Agriculture 5.0 security.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc2332609 |
| Date | 05 1900 |
| Creators | Bissadu, Kossi Dodzi |
| Contributors | Hossain, Gahangir, Hossain, Tozammel, Hawamdeh, Suliman M., Anderson, Richard |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Bissadu, Kossi Dodzi, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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