Optimal decision-making in farming has long been a challenging task. Each stage of the process demands optimization considering constraints such as seed genetics, soil chemistry, weather variability, prevalent pests and diseases, and the biochemical nature of fertilizers and pesticides. Unfortunately, the relevant information for these constraints has been scattered across various documents and webpages, often too technical for farmers to derive actionable steps and not delivered when they are needed.
At Agrichat, we're pioneering the use of LLM-powered Agents that consolidate all the relevant information in one place. These Agents analyze the data, deducing optimal and actionable steps, delivering them to farmers in a conversational and timely manner.
The use of these Agents is optimal in the agricultural sector, a domain that is increasingly more complex and dynamic due to climate change, and one in which operators are unevenly skilled.