  Agriculture, vital for global sustenance, necessitates innovative solutions
due to a lack of organized domain experts, particularly in developing countries
where many farmers are impoverished and cannot afford expert consulting.
Initiatives like Farmers Helpline play a crucial role in such countries, yet
challenges such as high operational costs persist. Automating query resolution
can alleviate the burden on traditional call centers, providing farmers with
immediate and contextually relevant information. The integration of Agriculture
and Artificial Intelligence (AI) offers a transformative opportunity to empower
farmers and bridge information gaps. Language models like transformers, the
rising stars of AI, possess remarkable language understanding capabilities,
making them ideal for addressing information gaps in agriculture. This work
explores and demonstrates the transformative potential of Large Language Models
(LLMs) in automating query resolution for agricultural farmers, leveraging
their expertise in deciphering natural language and understanding context.
Using a subset of a vast dataset of real-world farmer queries collected in
India, our study focuses on approximately 4 million queries from the state of
Tamil Nadu, spanning various sectors, seasonal crops, and query types.
