Analyzing and Mitigation of Cybersecurity Threats and Risks in Precision Agriculture Through IoT and AI Techniques
TL;DR
A comprehensive framework for analyzing CS challenges in PA is introduced by systematically identification and classification key CS parameters and its sub-parameters, threats, risks, and their potential impacts across advanced technique and drone-enabled agricultural settings.
Abstract
Precision agriculture (PA) increasingly relies on advanced techniques with drone machine based-technique to enhanced agricultural productivity, resource efficiency, and sustainability. Despite these significant, the widespread deployment of interconnected advanced farming systems has presented vital cybersecurity (CS) vulnerabilities that threaten data integrity, operational continuity, and decision-making processes. This researcher study introduced a comprehensive framework for analyzing CS challenges in PA by systematically identification and classification key CS parameters and its sub-parameters, threats, risks, and their potential impacts across advanced technique and drone-enabled agricultural settings. This approach evaluates the severity of CS risks, highlights vital vulnerabilities in existing PA infrastructures, and emphasizes the necessity for domain-specific security mechanism tailored to advanced farming ecosystems. The findings focused on securing interconnected agricultural devices, protecting sensitive farming information, and mitigating emerging cyber threats remain major challenges for sustainable PA deployment. Further, in this study proposes a future research roadmap that integrates advanced security techniques and real-time mitigation mechanisms to improving the resilience, reliability, and trustworthiness of PA systems. The proposed comprehensive framework provides researchers and expert with a structured foundation for strengthening CS and supporting the secure, sustainable adoption of next-generation modern agriculture techniques.