Abstract

Smart farming is increasingly recognized as a key pathway to sustainable crop production under conditions of climate variability, resource scarcity, labor shortages, and rising food demand. This review critically examines emerging technologies, artificial intelligence (AI), the Internet of Things (IoT), robotics, and precision agriculture, and their contributions to efficient, resilient, and environmentally sustainable farming systems. Using a PRISMA-guided systematic approach, 40 studies were selected from an initial pool of 3,630 records and analyzed through qualitative meta-synthesis. The findings indicate that IoT-based sensing technologies form the operational backbone of smart farming by enabling real-time monitoring of soil, crops, and environmental conditions. AI and machine learning act as the analytical layer, converting these data into decision-support tools for irrigation scheduling, disease detection, yield prediction, fertilizer optimization, and stress management. Robotics, drones, and autonomous systems extend these capabilities into field operations through timely, site-specific interventions. The literature also highlights a transition from isolated digital tools to integrated smart farming systems, including digital twins, embedded intelligence, and adaptive automation associated with Agriculture 5.0. Across the reviewed studies, smart farming consistently improves resource-use efficiency, productivity, environmental performance, labor efficiency, and climate resilience. However, adoption remains constrained by high implementation costs, infrastructure limitations, interoperability challenges, limited explainability of AI systems, and unresolved policy and governance issues. As a whole, smart farming is transforming agriculture from input-intensive to knowledge-intensive systems, with long-term sustainability dependent on affordable, interoperable, and human-centered implementation strategies.