Building AI-based advisory services for smallholder farmers: Technical learnings from the AIEP Initiative
Stewart Collis (1), Florence Kinyua (2), Vikram Kumar (2), Howard Lakougna (1), Christian Merz (2), Kirti Pandey (2), Christian Resch (3) (on behalf of the AIEP Initiative, (1) Gates Foundation, (2) Deutsche Gesellschaft f\"ur International Zusammenarbeit, (3) CLEAR Global)

TL;DR
This paper shares technical insights from deploying AI advisory services for smallholder farmers in Kenya and India, highlighting architecture, challenges, and evaluation methods to improve accessibility and effectiveness.
Contribution
It introduces a modular AI system architecture for agricultural advice and discusses key challenges and enablers, including multilingual voice access and corpus curation, with evaluation benchmarks.
Findings
High user satisfaction (NPS ~60) among farmers.
Latency reduction strategies achieved consistent under 5 seconds.
Identified key challenges in language coverage and corpus curation.
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…
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Taxonomy
TopicsICT in Developing Communities · Smart Agriculture and AI · AI in Service Interactions
