Leveraging IoT and Smart Sensors in Agriculture: Opportunities and Challenges

Abstract

The rapid adoption of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and advanced sensing technologies is transforming modern agriculture by improving crop monitoring, disease detection, irrigation management, and resource utilization. This paper presents a comprehensive review of recent IEEE conference and journal publications on IoT-enabled smart agriculture, highlighting developments in environmental monitoring, wireless sensor networks, mobile vision systems, cloud and edge computing, and data analytics. The reviewed studies demonstrate that integrating IoT with ML enables real-time decision-making, enhances crop health assessment, improves yield prediction, and supports precision farming practices. Recent advances in communication technologies such as LoRa, 5G, and NFC have further strengthened connectivity and scalability in agricultural applications, while blockchain-based frameworks have emerged as promising solutions for secure data management and traceability. Despite these advancements, challenges including high deployment costs, interoperability, limited rural connectivity, and data security continue to hinder large-scale adoption. This review summarizes the major technologies, applications, benefits, and limitations reported in the literature and identifies future research directions, including affordable IoT solutions, explainable AI models, integrated edge-cloud architectures, and secure, scalable smart farming platforms for sustainable agriculture. This paper is a review study that synthesizes recent advances in IoT, machine learning, and smart sensors for agriculture based on published literature. It does not present a new system implementation or experimental validation.