Machine Learning-Driven Predictive Analytics for Smart Agriculture Using IoT Sensor Networks

Abstract

Machine learning-driven predictive analytics has become a central enabling capability in smart agriculture because it converts heterogeneous, high-frequency observations from Internet of Things (IoT) sensor networks into operational recommendations for irrigation, fertilisation, disease control, yield management, and risk mitigation. Recent literature shows that the field has progressed from isolated sensing deployments toward integrated cyber-physical systems that combine in-field sensors, edge devices, cloud platforms, and decision support modules for precision interventions. At the same time, the transition from descriptive monitoring to predictive and prescriptive intelligence remains constrained by data quality, infrastructure fragmentation, weak interoperability, and limited generalisation across crops, geographies, and farm scales. This review synthesises recent developments in machine learning-enabled predictive analytics for smart agriculture with particular emphasis on IoT sensor networks, smart sensing modalities, edge–cloud architectures, benchmark datasets, algorithmic design, and deployment challenges. The article develops a taxonomy spanning sensing layers, prediction tasks, learning paradigms, deployment architectures, and evaluation criteria, then critically examines supervised, ensemble, deep, temporal, federated, and explainable learning approaches used for agricultural forecasting. It also discusses system architectures that integrate wireless sensor networks, remote sensing streams, edge inference, and cloud orchestration, highlighting trade-offs among latency, energy consumption, robustness, and scalability. Comparative analysis demonstrates that no single model family is universally optimal; performance depends strongly on sensing fidelity, temporal granularity, environmental drift, and operational cost. The review further addresses applications in irrigation scheduling, disease and pest surveillance, yield prediction, nutrient management, and climate resilience, and it situates these applications within broader agrifood transformation efforts supported by international institutions such as FAO, which frames digital agriculture and AI as instruments for more efficient, sustainable, and resilient agrifood systems. The article concludes by identifying research opportunities in multimodal foundation models, causality-aware agronomic learning, privacy-preserving federation, trustworthy edge AI, and inclusive data governance for smallholder-centred deployment.