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Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers

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arxiv 2409.08916 v2 pith:ZVRNGG2A submitted 2024-09-13 cs.ET cs.AIcs.HC

Namita Singh , Jacqueline Wang'ombe , Nereah Okanga , Tetyana Zelenska , Jona Repishti , Jayasankar G K , Sanjeev Mishra , Rajsekar Manokaran
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Vineet Singh Mohammed Irfan Rafiq Rikin Gandhi Akshay Nambi
This is my paper · ORCID
classification cs.ETcs.AIcs.HC
keywords farmerchatagriculturalai-poweredeffectivenessfarmersgenerativehighlightsscalability
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Small and medium-sized agricultural holders face challenges like limited access to localized, timely information, impacting productivity and sustainability. Traditional extension services, which rely on in-person agents, struggle with scalability and timely delivery, especially in remote areas. We introduce FarmerChat, a generative AI-powered chatbot designed to address these issues. Leveraging Generative AI, FarmerChat offers personalized, reliable, and contextually relevant advice, overcoming limitations of previous chatbots in deterministic dialogue flows, language support, and unstructured data processing. Deployed in four countries, FarmerChat has engaged over 15,000 farmers and answered over 300,000 queries. This paper highlights how FarmerChat's innovative use of GenAI enhances agricultural service scalability and effectiveness. Our evaluation, combining quantitative analysis and qualitative insights, highlights FarmerChat's effectiveness in improving farming practices, enhancing trust, response quality, and user engagement.

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Cited by 1 Pith paper

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  1. Pezego-HITL: A policy-grounded large language model architecture for agricultural extension in Ghana

    cs.MA 2026-07 conditional novelty 6.0 of 10

    A policy-grounded, cache-routed LLM architecture with human-in-the-loop verification reports PAR 0.94 and 55% lower P95 latency on simulated Ghanaian farm queries.

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