Agentic AI-Based IoT Precision Agriculture Framework—Our Vision and Challenges

Danco Davcev; Slobodan Kalajdziski; Ivica Dimitrovski; Ivan Kitanovski; Kosta Mitreski

doi:10.3390/agriengineering8040147

journal article Open Access Apr 09, 2026

Agentic AI-Based IoT Precision Agriculture Framework—Our Vision and Challenges

Danco Davcev

Slobodan Kalajdziski

Ivica Dimitrovski

Ivan Kitanovski

Kosta Mitreski

AgriEngineering Vol. 8 No. 4 pp. 147 · MDPI AG

View at Publisher Save 10.3390/agriengineering8040147

Abstract

Accurate, timely, and resource-efficient decision-making is critical for sustainable precision agriculture. This paper proposes an agentic AI-based Internet of Things (IoT) framework that enables coordinated, closed-loop perception–decision–action processes across heterogeneous sensing and actuation components. The framework models agricultural systems as distributed collections of goal-driven agents responsible for multimodal sensing, uncertainty-aware reasoning, and adaptive decision-making. To provide a structured foundation, the proposed architecture is formalized within a Multi-Agent Partially Observable Markov Decision Process (MPOMDP) perspective, enabling systematic treatment of coordination, uncertainty, and decision policies. The framework integrates multimodal information sources, including vision-based perception and environmental sensing, and defines mechanisms for their fusion and use in system-level decision-making. A proof-of-concept instantiation is presented using publicly available datasets, combining visual perception models and tabular reasoning models within the proposed agentic workflow. The experiments are designed to demonstrate the feasibility, modularity, and coordination capabilities of the framework, rather than to benchmark predictive performance or provide field-validated evaluation. The results illustrate how multimodal information can be integrated to support adaptive and resource-aware decision processes. Finally, the paper discusses key challenges and outlines directions for future work, including real-world deployment, integration with physical actuation systems, and validation under operational conditions.

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Published: Apr 09, 2026
Vol/Issue: 8(4)
Pages: 147
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Authors

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Danco Davcev