Edge AI–IoT Integration for Real-Time Precision Farming : A Framework for Adaptive Monitoring, Prediction, and Resource Optimization - Adewumi, Idowu Olugbenga; Adejumo, Akinfoye Oyime Daniel; Adegbokan, Oluwatoyin; Ogundare, Ebenezer Oluwaponmile (London Journal of Physics, 2026)
This research offers a thorough Edge AI–IoT framework for real-time precision agriculture, aimed at facilitating adaptive oversight, forecasting analysis, and resource efficiency. The suggested framework combines edge AI, UAV-based sensing, and IoT-enabled data collection to facilitate context-aware and time-sensitive decision-making in agricultural activities. The framework’s performance was evaluated against IoT-only and cloud AI setups using metrics including latency, forecasting precision, pest identification, yield prediction, and resource effectiveness. Experimental assessment showed a 76% decrease in latency when compared to IoT-only systems (145 ms vs. 610 ms) and an 82% enhancement over cloud AI (145 ms vs. 820 ms). The error in soil moisture forecasting decreased by 54% (RMSE = 0.028 compared to 0.061), whereas pest detection performance rose from F1 = 0.72 to 0.91 (+26%). The precision of yield estimation rose from R² = 0.65 to 0.87 (+34%), along with significant decreases in resource consumption—water by 19%, fertilizer by 11%, and pesticide by 8%—leading to a total cost reduction of 14%. Scalability evaluations further validated system strength, with throughput increasing from 280 to 2,050 records per second as sensor nodes rose from 20 to 200, all while keeping latency below 420 ms and packet loss under 2.1%. These findings confirm that the integration of Edge AI and IoT greatly improves inference speed, predictive accuracy, and operational effectiveness for both smallholder and large-scale farms. Although there are obstacles concerning energy consumption, connectivity, and the expense of UAVs, the research emphasizes solar-powered sensors and collaborative UAV services as practical facilitators for the sustainable integration of data-driven agriculture