Retrieval-grounded multilingual LLM assistance for island smallholder farmers
Abstract
Background Smallholder farmers on remote, depopulating islands have limited access to advisory services, and their locally specific agronomic knowledge, often expressed in regional dialect, is poorly represented in the corpora on which large language models are trained. A general-purpose chatbot therefore answers local questions fluently but unreliably. A conversational artificial intelligence assistant, Falco eleonorae , was developed for the farmers and cooperatives of Kythera and Antikythera, Greece. Methods The assistant is a thin Backend-for-Frontend proxy in front of a managed, geospatially aware agronomic agent, hosting no chat model of its own. Answer generation and tool selection are delegated to upstream models; a vision-capable model turns an uploaded field photograph into a short description, so only text reaches the agent; voice is transcribed by a managed speech-to-text service in a European Union region. Grounding uses tool-augmented retrieval rather than a self-hosted vector database: a Model Context Protocol tool queries a curated, read-only, bilingual data interface, and every request carries a geospatial Well-Known Text envelope anchoring the agent to the islands. Results It is deployed as a Greek-primary progressive web application supporting text, voice, and image input with streamed responses, suited to low digital literacy and poor connectivity. Locally scoped questions are answered from the pilot’s editorially controlled records rather than the…
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