# AgriLLM: Harnessing Transformers for Farmer Queries

arXiv: https://arxiv.org/abs/2407.04721 · HTML: https://mlsanity.com/paper/2407.04721/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Causal Autoregressive Flows](https://mlsanity.com/paper/2011.02268/) (2020)
- [IndicBART: A Pre-trained Model for Indic Natural Language Generation](https://mlsanity.com/paper/2109.02903/) (2021)
- [Neural Machine Translation and Sequence-to-sequence Models: A Tutorial](https://mlsanity.com/paper/1703.01619/) (2017)
- [AgAsk: An Agent to Help Answer Farmer's Questions From Scientific Documents](https://mlsanity.com/paper/2212.10762/) (2022)
- [Evolution of transfer learning in natural language processing](https://mlsanity.com/paper/1910.07370/) (2019)
- [Farmer-Bot: An Interactive Bot for Farmers](https://mlsanity.com/paper/2204.07032/) (2022)
- [Chatbot Application to Support Smart Agriculture in Thailand](https://mlsanity.com/paper/2308.02524/) (2023)

## Built on by (most cited first, 2 of 2)

- [AgriGPT-Omni: A Unified Speech-Vision-Text Framework for Multilingual Agricultural Intelligence](https://mlsanity.com/paper/2512.10624/) (2025)
- [Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering](https://mlsanity.com/paper/2508.03719/) (2025)

## Abstract (arXiv)

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.