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

Summary

This paper presents a conceptual and technical framework for applying agentic artificial intelligence and Internet of Things technologies to precision agriculture, proposing a Multi-Agent Partially Observable Markov Decision Process architecture to coordinate distributed sensing, reasoning, and adaptive decision-making. Rather than reporting field-validated agronomic outcomes, the work demonstrates proof-of-concept implementation using public datasets and vision-based perception models, with emphasis on architectural modularity and information fusion. The authors acknowledge that real-world deployment, physical actuation integration, and operational validation remain key future challenges.

Regional applicability

As a methodological and architectural contribution focused on AI/IoT systems rather than agronomic validation, direct applicability to United Kingdom farming practice cannot yet be assessed. Transferability of the framework would depend on successful real-world deployment and integration with UK-relevant cropping systems, soil conditions, and climatic contexts, which the authors identify as future work.

Key measures

Framework architecture formalisation; multimodal information fusion mechanisms; agent coordination capabilities; closed-loop perception–decision–action process integration

Outcomes reported

The study proposes and presents a proof-of-concept instantiation of an agentic AI-based IoT framework that integrates multimodal sensing (vision and environmental) with adaptive decision-making using a Multi-Agent Partially Observable Markov Decision Process (MPOMDP) architecture. Experiments demonstrate the feasibility, modularity, and coordination capabilities of the framework using publicly available datasets.

Topic tags