AgriLLM: Harnessing Transformers for Farmer Queries

TL;DR

This paper explores the use of transformer-based large language models to automate and improve the resolution of farmer queries in agriculture, aiming to provide immediate, relevant information and reduce operational costs.

Contribution

It demonstrates the application of LLMs to real-world agricultural queries, showcasing their potential to bridge information gaps for farmers in developing regions.

Findings

Successfully processed 4 million farmer queries from Tamil Nadu.

Showcased the ability of LLMs to understand diverse agricultural questions.

Highlighted potential cost reductions in farmer support services.

Abstract

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.