Letting the Crop Speak: A Proof-of-Concept Sensor-Grounded Large Language Model Advisor for Potato Irrigation on a Mediterranean Vertisol

Low-cost soil sensors are now widespread, but turning a moisture reading into an irrigation decision still requires expertise that many growers lack. This proof-of-concept study describes a conversational advisor in which a general-purpose large language model (GPT-5) receives live readings from a low-cost capacitive probe, together with a short crop and soil profile, and answers the operator in the first person, as the plant. The advisor was used throughout a 14-week greenhouse potato (Solanum tuberosum L.) cycle in a clay-rich Moroccan Vertisol (“Tirs”). Six pots were irrigated only on its recommendation and six by an experienced technician working without sensor data. The advisor-guided block received less water, and its plants grew taller and set more tubers of larger maximum diameter. Because the treatments were neither replicated nor randomised, irrigation was logged per block and tuber mass was not recorded, these observations are descriptive and cannot be attributed to the language model rather than to the sensor, bench position or irrigation schedule. The recorded dialogue showed the advisor reasoning faithfully from its inputs, including a mislabelled moisture unit. The study demonstrates technical feasibility, not the agronomic effectiveness of the recommendations, and defines the controls a replicated trial requires.