Statistical Analytical Review of AI–IoT Integration for Smart Agriculture: Numerical Insights, Performance Metrics, and Future Vision Analysis

Abstract

AI, IoT, blockchain, and edge–fog–cloud computing are revamping agriculture, yet studies are dispersed and lacking in quantitative detail. This statistical study of peer-reviewed papers includes performance-based synthesis of key Agriculture 4.0 technologies. Each study evaluated latency, dependability, energy efficiency, scalability, security, and accuracy to numerically compare 5G–UAV IoT systems, fog-driven field tracking, blockchain-enabled smart contracts, and AI-powered anomaly detections. The review used leading database literature, method categorization and performance metric extraction, and cross-domain statistical consolidations. Quantitative studies show next-generation agricultural IoT systems can scale beyond 10,000 devices, with sub-30 ms latencies, >92% prediction accuracy, 18–35% energy savings, and sub-30 m for the process. By combining numerical trends with architectural strengths and weaknesses, this study finds technological gaps in real-field multi-season Validation, energy–security co-optimization, and lightweight edge-native AI Sets. The report provides a baseline for evaluating agricultural IoT technologies to help researchers, industry practitioners, and regulators make informed decisions. This study combines scattered knowledge into a statistical foundation and proposes sustainable computing, quantum-resilient security, and ethical governance for smart agriculture deployments.