Smart Agro-ecosystem: A Review of LLM-based Robotic Systems for Sensing and Decision Support in Precision Agriculture

Smart agriculture employs emerging technologies to address main challenges such as greenhouse gases emissions, crop yield optimization, and efficient irrigation through optimized resource planning. This paper reviews Large Language Models (LLMs), robots, and multi-sensor data fusion by combining information from multiple sensors to detect real-time gas emissions for decision support in Agricultural Cyber Physical System (ACPS). LLMs are distinct from earlier AI because they understand context and can process data from IoT sensors, drones, and satellites into useful advice for farmers. Robots with gas sensors can effectively monitor the emissions such as CO2, CH4, NH3, and NO2, while adaptive algorithms improve resilience under dynamic field conditions. This paper systematically reviews the current trends in LLM-based natural language processing (NLP) systems, multimodal architectures, and fusion strategies for distributed intelligence. Some case studies are used to demonstrate practical scenarios within the ACPS domain, highlighting AI deployment benefits, e.g. improved environmental parameters and better compliance with climate regulations. Future directions include swarm robotics for scalable monitoring, edge AI for real-time inference and lightweight LLMs for resource-constrained embedded cyber-physical systems. By compiling state-of-the-art research, this review establishes a road-map for LLM-driven, robotics-enabled ecosystem offering transformative potential for climate-smart, resilient agriculture.