## 8 Conclusions  
In this review, a field trimming with potential is revealed, yet grappling with fundamental challenges as it strives towards truly autonomous irrigation. While the integration of IoT, machine learning, and cloud technologies promises optimized water use, increased yields, and reduced labor, the journey toward widespread adoption faces significant obstacles. A key takeaway is the disconnect between technological capability and practical implementation. The case studies demonstrate impressive successes, achieving significant water savings and showcasing the power of data-driven, closed-loop control. However, these examples are often confined to specific contexts and struggle to scale effectively. The cost of upgrading legacy systems, particularly for smallholder farmers, remains a formidable barrier. Furthermore, the lack of standardized data formats and communication protocols hinders interoperability, creating a fragmented landscape of proprietary solutions that limit functionality and increase complexity. Perhaps the most pressing concern is the vulnerability of these interconnected systems to cyberattacks. Ensuring robust cybersecurity measures becomes paramount as we entrust critical infrastructure to data-driven automation. The potential consequences of breaches extend far beyond economic losses, threatening food security and environmental sustainability. The path forward requires a shift from isolated technological advancements to a more holistic and collaborative approach. Future research and development must prioritize human-centered design, ensuring that interfaces and decision-support tools are intuitive and tailored to farmers' needs. Robustness and resilience must be built into system architectures, incorporating redundancy, fault-tolerance, and self-healing capabilities to guarantee reliable operation. Decentralized architectures leveraging edge computing and advanced AI can empower localized control, reducing reliance on continuous cloud connectivity. Critically, developing comprehensive cybersecurity frameworks is non-negotiable. Finally, embracing open standards and protocols is crucial for fostering interoperability, reducing vendor lock-in, and accelerating innovation. Our analysis of implemented systems highlights several practical pathways for adoption. The most successful deployments typically begin with basic monitoring and control infrastructure, gradually expanding capabilities as operational benefits are demonstrated. Edge computing solutions have proven particularly valuable for areas with limited connectivity, while cloud-integrated systems offer the highest degree of automation for operations with robust infrastructure. The choice between these approaches should be guided by local conditions, existing infrastructure, and organizational capacity for technical support. This staged implementation strategy helps organizations build expertise while managing costs and complexity. The transition to truly autonomous and intelligent irrigation demands a collaborative effort that transcends disciplinary boundaries. Researchers, technology developers, policymakers, and farmers must work together to address the technical, social, economic, and ethical dimensions of this transformative shift. By prioritizing human needs, fostering robust security, and promoting open collaboration, we can unlock the full potential of automated irrigation, paving the way for a more sustainable and equitable agricultural future. Conceptualization, B.N. and A.K. with input from D.M.H., H.G. and D.R.R. ; methodology, B.N. and A.K. ; investigation, B.N. ; resources, B.N. and A.K. ; data curation, B.N. ; writing--original draft preparation, B.N. ; writing--review and editing, B.N. and A.K. with input from H.N.N., D.M.H., X.Q., Y.G., D.R.R., J.W., E.B. and S.K. ; visualization, B.N. ; supervision, A.K. ; project administration, B.N. and A.K. ; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.