Cybersecurity and Privacy in AI-Enabled Agricultural IoT Ecosystems: A Systematic Review of Threats, Safeguards, and Resilience Gaps

Agricultural Internet of Things (IoT) ecosystems increasingly connect sensors, drones, edge devices, and cloud platforms to support precision farming, yet cybersecurity, privacy, and the real-world readiness of proposed safeguards remain fragmented across the literature. This study systematically reviewed cybersecurity threats, privacy concerns, AI-driven and traditional safeguards, and evidence gaps in agricultural IoT research published between 2015 and 2025. Following the Kitchenham and Charters methodology, 103 studies were selected from 2535 records retrieved across five databases. STRIDE and LINDDUN were retrospectively applied as complementary frameworks for threat and privacy classification. Because the coding scheme was multi-label, reliability was assessed at the category level using presence/absence decisions on a 20-study sample and observed agreement ranged from 75% to 95% for STRIDE and 95% to 100% for LINDDUN, with interpretable Cohen’s κ values ranging from 0.348 to 0.794 and 0.875 to 1.000, respectively. All included studies also underwent quality appraisal and a supplementary ecological-validity assessment. Denial-of-service, tampering, and spoofing were the most frequently reported threats, concentrated at the device, network, and cloud layers, while the edge layer remained underexamined. AI- and machine-learning-based intrusion detection and privacy-preserving methods such as federated learning emerged as prominent safeguards, but adversarial manipulation of agricultural AI models received limited attention. Privacy research remained oriented toward confidentiality, with 90.3% of studies referencing no applicable regulatory framework. Most importantly, only 8 of 103 studies (7.8%) received a High ecological-validity rating, showing how rarely the evidence base is grounded in real agricultural field conditions. The review identifies field-grounded evaluation, adversarially robust AI, privacy governance, and cyber resilience as priorities for future agricultural IoT security research.