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        <title>Enhancing AI-Driven Farming Advisory in Kenya with Efficient RAG Agents via Quantized Fine-Tuned Language Models</title>
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        <namePart type="given">Theophilus</namePart>
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            <title>Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)</title>
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    <abstract>The integration of Artificial Intelligence (Al) in agriculture has significantly impacted decision making processes for farmers, particularly in regions such as Kenya, where access to accurate and timely advisory services is crucial. This paper explores the deployment of Retrieval Augmented Generation (RAG) agents powered by fine-tuned quantized language models to enhance Al-driven agricultural advisory services. By optimizing model efficiency through quantization and fine-tuning, our aim is to deliver a specialized language model in agriculture and to ensure real-time, cost-effective and contextually relevant recommendations for smallholder farmers. Our approach takes advantage of localized agricultural datasets and natural language processing techniques to improve the accessibility and accuracy of advisory responses in local Kenyan languages. We show that the proposed model has the potential to improve information delivery and automation of complex and monotonous tasks, making it a viable solution to sustainable agricultural intelligence in Kenya and beyond.</abstract>
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%0 Conference Proceedings
%T Enhancing AI-Driven Farming Advisory in Kenya with Efficient RAG Agents via Quantized Fine-Tuned Language Models
%A Owiti, Theophilus Lincoln
%A Kipkebut, Andrew Kiprop
%Y Lignos, Constantine
%Y Abdulmumin, Idris
%Y Adelani, David
%S Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-257-2
%F owiti-kipkebut-2025-enhancing
%X The integration of Artificial Intelligence (Al) in agriculture has significantly impacted decision making processes for farmers, particularly in regions such as Kenya, where access to accurate and timely advisory services is crucial. This paper explores the deployment of Retrieval Augmented Generation (RAG) agents powered by fine-tuned quantized language models to enhance Al-driven agricultural advisory services. By optimizing model efficiency through quantization and fine-tuning, our aim is to deliver a specialized language model in agriculture and to ensure real-time, cost-effective and contextually relevant recommendations for smallholder farmers. Our approach takes advantage of localized agricultural datasets and natural language processing techniques to improve the accessibility and accuracy of advisory responses in local Kenyan languages. We show that the proposed model has the potential to improve information delivery and automation of complex and monotonous tasks, making it a viable solution to sustainable agricultural intelligence in Kenya and beyond.
%R 10.18653/v1/2025.africanlp-1.5
%U https://aclanthology.org/2025.africanlp-1.5/
%U https://doi.org/10.18653/v1/2025.africanlp-1.5
%P 24-30

Markdown (Informal)

[Enhancing AI-Driven Farming Advisory in Kenya with Efficient RAG Agents via Quantized Fine-Tuned Language Models](https://aclanthology.org/2025.africanlp-1.5/) (Owiti & Kipkebut, AfricaNLP 2025)

• Enhancing AI-Driven Farming Advisory in Kenya with Efficient RAG Agents via Quantized Fine-Tuned Language Models (Owiti & Kipkebut, AfricaNLP 2025)

ACL

• Theophilus Lincoln Owiti and Andrew Kiprop Kipkebut. 2025. Enhancing AI-Driven Farming Advisory in Kenya with Efficient RAG Agents via Quantized Fine-Tuned Language Models. In Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025), pages 24–30, Vienna, Austria. Association for Computational Linguistics.