Internet of Things-Based Soil Nutrient Monitoring Systems for Crop Yield Prediction: A PRISMA Systematic Review of Methodologies, Machine Learning Models, and Research Gaps

Abstract

Global food production must rise by at least 60% by 2050, yet the inputs needed to reach that target risk accelerating the soil degradation that limits yields in the first place. The Internet of Things (IoT) and Machine Learning (ML) are increasingly used to monitor soil health and forecast crop performance in response to this pressure. Despite growing research in this area, no comprehensive synthesis has yet mapped the common methodologies in use, and it remains unclear how consistently different ML models perform once deployed across varied farming environments — a gap this review addresses. Using a PRISMA-compliant systematic review of 87 peer-reviewed studies published between 2017 and  2026, this paper examines how IoT sensor networks, wireless communication protocols, cloud architectures, and ML models are being combined to support precision agriculture. This review covers sensor deployment techniques, wireless protocols (WiFi, LoRaWAN, and NB- IoT), and ten standard ML models for multiple crop types, with the random forest regression (RFR) showing the best performance with an R2 of 0.97 across the datasets considered. The review also covers how explainable AI techniques can help farmers to comprehend and have confidence in results from model. Three common gaps are identified: a lack of interdisciplinary validation, a lack of studies from Sub-Saharan Africa, and a lack of a standardized approach to IoT sensor calibration. These findings offer a reference point for researchers, policymakers, and developers of future smart-farming systems.