Utilization of the Internet of Things (IoT) in Precision Agriculture to Increase Productivity and Efficiency of Land Processing

Objective: This study aims to systematically review the development and application of the Internet of Things (IoT) in precision agriculture and to identify its role in improving agricultural productivity, land management efficiency, and sustainable agricultural practices.
Research Design & Methods: This research utilized a Systematic Literature Review (SLR) following the PRISMA methodology. Relevant articles were sourced from the Scopus database through specific search terms associated with IoT and precision agriculture. Following the application of inclusion and exclusion criteria, 33 peer-reviewed papers were chosen for qualitative synthesis. The chosen studies underwent thematic analysis to uncover IoT technologies, agricultural applications, implementation results, and emerging research trends.
Findings: The Internet of Things (IoT) serves as a critical enabling technology for precision agriculture, encompassing sensors, wireless sensor networks (WSNs), edge and cloud computing, artificial intelligence (AI), and machine learning (ML). IoT improves decision-making, optimizes water, fertilizer, and pesticide use, increases productivity, lowers operational costs, and promotes sustainable agricultural practices. However, its adoption is hampered by significant implementation costs, inadequate digital infrastructure, interoperability issues, data security concerns, and farmers' technological readiness.
Implications: These findings highlight the strategic role of IoT in accelerating the transition to Smart Agriculture and Agriculture 4.0. Wider adoption of IoT requires improvements in digital infrastructure, technology standardization, capacity building for farmers, and supportive government policies.
Contribution & Value Added: This study provides a comprehensive synthesis of IoT applications in precision agriculture by identifying key technologies, application areas, benefits, and implementation challenges, offering a reference for future research and the development of sustainable smart farming systems.