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arXiv · 2601.11537

Building AI-based advisory services for smallholder farmers: Technical learnings from the AIEP Initiative

Stewart Collis·Florence Kinyua·Vikram Kumar·Howard Lakougna·Christian Merz·Kirti Pandey·Christian Resch
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Abstract

We report technical learnings from five AI-based agricultural advisory MVPs deployed in Kenya and Bihar, India, under the AIEP Initiative. A 800-farmer study found high user satisfaction (NPS ~60). All solutions implement a modular two-part architecture: (i) an interface component (IVR /WhatsApp / app) with ASR-MT-TTS for multilingual voice access; and (ii) a reasoning component combining LLMs capabilities with query orchestration, external data (weather/soil/markets), and RAG over curated agricultural corpora. We describe key challenges: (a) latency, especially for voice; reductions were achieved via in-country hosting and audio minimization, but consistent <5s remains challenging; (b) language coverage: low-resource ASR/MT integration and nonstandard scripts hinder end-to-end quality; and (c) corpus curation: access, validation, and maintenance are labor-intensive, as well as provide recommendations on how to develop similar systems. We discuss common enablers including (a) data sharing, (b) common corpora, (c) better language AI and (d) evaluation and benchmarking. We also present golden Q&A sets to evaluate LLM capabilities for smallholder agriculture.

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Stewart Collis, Florence Kinyua, Vikram Kumar, Howard Lakougna, Christian Merz, Kirti Pandey, Christian Resch. 2025-11-27. Building AI-based advisory services for smallholder farmers: Technical learnings from the AIEP Initiative. https://arxiv.org/abs/2601.11537

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