Review Of Ai-Driven Farmer Support Systems Using Llm’s and Real-Time Data Integration

Abstract

Agriculture plays a vital role in economic development and food security, particularly in developing countries where farmers rely on timely and accurate agricultural guidance. Conventional advisory systems generally provide static and generalized information, limiting their effectiveness in real-time decision-making and personalized support. Recent advancements in Artificial Intelligence (AI), Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multimodal learning have enabled the development of intelligent and interactive agricultural advisory systems capable of delivering context-aware recommendations. This review paper presents a comprehensive overview of AI-based farmer advisory systems with a focus on conversational AI, retrieval-based frameworks, tool-augmented reasoning, and image-based crop disease detection. The study reviews existing research on machine learning-based crop recommendation systems, IoT-enabled smart agriculture, deep learning approaches, and LLM-powered agricultural assistants. The role of vector databases, external tool integration, and real-time data processing in improving the accuracy, relevance, and personalization of agricultural responses is also examined. Furthermore, the paper discusses major challenges, limitations, and future research opportunities associated with designing efficient, scalable, and reliable smart agricultural advisory systems. The review highlights the growing potential of integrating LLMs with retrieval mechanisms and multimodal technologies to enhance the effectiveness of next-generation agricultural support systems.