AgriTech: Foundations, Emerging Technologies, System Architectures, and Deployment Readiness—A Comprehensive Survey

Agricultural technology (AgriTech) is transforming agricultural production through advances in artificial intelligence, Internet of Things, robotics, remote sensing, digital twins, and intelligent decision-support systems. Despite these developments, the transition from technological innovation to effective agricultural deployment remains constrained by technical, operational, economic, and institutional challenges. Existing research frequently emphasizes individual technologies, applications, or performance indicators, providing limited integration of system architectures, operational reliability, affordability, interoperability, and deployment readiness. This paper presents a comprehensive survey of AgriTech foundations, enabling technologies, architectural approaches, agricultural applications, and performance evaluation methods. It examines centralized, distributed, cloud, edge, and hybrid architectures alongside intelligent agricultural systems and autonomous technologies. A comparative analysis identifies their capabilities, limitations, infrastructure requirements, and operational trade-offs. The survey further investigates agricultural data quality, environmental variability, cybersecurity, maintenance, financial sustainability, digital inclusion, and governance challenges affecting practical implementation. Building on this synthesis, the paper proposes the Integrated AgriTech Effectiveness and Deployment Readiness Framework (IAEDRF), which connects agricultural requirements, technology selection, system integration, performance validation, economic feasibility, organizational preparedness, and responsible governance. The framework introduces context-dependent assessment criteria, mandatory deployment requirements, and continuous operational reassessment. Its proposed suitability model remains conceptual and requires empirical validation. Finally, the survey identifies research priorities involving generalizable agricultural AI, interoperable infrastructures, affordable technologies, real-world benchmarking, and sustainable scaling. The findings emphasize that AgriTech effectiveness depends not only on technological capability but also on reliable, affordable, interoperable, and context-appropriate deployment. The survey provides an integrated foundation for future research, technology development, and evidence-based agricultural implementation.