Leveraging AI chat assistants for enhanced food security in Africa: A comprehensive integration of large language models, retrieval augmented generation, and vector embedding techniques

Abstract

Agricultural productivity in Africa faces significant challenges due to limited access to timely, localized information and expert guidance, leading to suboptimal farming practices that threaten food security and economic development. The increasing demands of food security and sustainable agriculture necessitate cutting-edge technological solutions. This study introduces an Artificial Intelligence (AI) chat assistant powered by Large Language Models (LLMs) and AI agents, utilizing Retrieval-Augmented Generation (RAG) and vector database embeddings to transform agricultural practices. By integrating LLMs with domain-specific data through RAG, the assistant delivers precise, context-aware responses to complex agricultural inquiries. The use of vector embeddings enables efficient semantic search across extensive agricultural datasets, enhancing information retrieval and decision-making processes for frontline extension workers, agronomists, and farmers. The AI agents facilitate autonomous task execution, such as analysis of crop health, disease identification, and predictive modelling based on weather patterns. Natural language processing ensures intuitive user interactions, making advanced agricultural insights accessible regardless of technical proficiency. AI chat assistance leads to increased access to much-needed information on a wide range of agricultural practices, empowering farmers to optimize their practices, enhance crop yields, and significantly contribute to food security and sustainable economic development in Africa.