Smart Agriculture Through Internet of Things and Machine Learning: A Survey of Monitoring Techniques, Methods and Applications

ABSTRACT In today's digital era, the rapid proliferation of connected devices and the growing impact of artificial intelligence (AI) have created a powerful synergy, reshaping multiple sectors, including agriculture. This convergence has led to the rise of smart farming systems, where the Internet of things (IoT) enables large‐scale continuous data collection from diverse agricultural environments, whereas machine learning (ML) provides the analytical capacity to process, interpret and transform these data into actionable insights. Together, IoT and ML empower farmers and stakeholders to make informed timely decisions that improve efficiency, productivity and sustainability. This article explores the integration of IoT and ML in agricultural monitoring systems. It reviews major application domains, surveys data collection strategies using wireless sensors, aerial vehicles and animal monitoring systems and discusses underlying architectures, topologies and communication protocols. Furthermore, it examines ML methods applied to interpret data, optimise IoT networks and support decision‐making. Special focus is given to challenges such as high energy consumption, transmission limitations, routing efficiency, storage constraints, deployment complexity and the selection of suitable learning models. The findings highlight that IoT and ML alone are insufficient but, when combined, offer a promising pathway towards intelligent, adaptive and sustainable farming systems for future generations.