Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Localized agri-LLMs: exploring low-power, low-cost models for SSPs in Africa and India.

Domain:

agriculturenatural language processingdigital infrastructure

Record type:

paper
Creator:
MalMarAhmFem
Publisher:
CAB
Host:
Abstract The transformative potential of artificial intelligence (AI) in agriculture is increasingly recognized, yet most large language models (LLMs) remain inaccessible to small-scale producers (SSPs) in Africa and India due to high computational requirements, limited localization, and infrastructure constraints. Artificial intelligence in agriculture offers significant promise for smallholder farmers, agricultural extension systems, digital agriculture transformation, and agri-food system modernization; however, a widening gap persists between AI capability and real-world accessibility in low- and middle-income countries (LMICs). Most LLMs are designed for high bandwidth environments, trained on non-representative datasets that poorly reflect African agriculture and Indian agriculture systems, and priced beyond the reach of agricultural extension agents, farmer organizations, and cooperatives serving SSPs, resulting in structural exclusion of smallholder farmers. This paper examines the development and deployment of affordable, locally fine-tuned agricultural large language models (Agri-LLMs) for smallholder agriculture, agricultural advisory services, and digital extension systems through multi-stakeholder public-private partnerships integrated within agricultural extension systems and digital agriculture ecosystems. It reframes AI localization in agriculture as a systems-level challenge requiring alignment across AI model architecture, agricultural data governance, sustainable financing models, and policy frameworks. It further explores localized, low-power, cost-efficient AI systems supporting climate-smart agriculture, precision agriculture, and farmer decision-support systems tailored to linguistic, cultural, and agronomic diversity in Africa and India, drawing on India's Digital Public Infrastructure (DPI) for agriculture and Africa's emerging agri-tech and AI innovation ecosystems.

Visit

doi.org

Tasks

language modeling

Licenses

https://www.cabidigitallibrary.org/text-and-data-mining

Similar

Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMsEricPeter/LLMs-Low-ResourcedPrompt engineering on large language models (LLMs) in low-resourced language settingQuantized deep learning models on low-power edge devices for robotic systemsLLM Probe: Evaluating LLMs for Low-Resource Languageschinmayjainnnn/LLMs-for-Translation-of-Low-Resource-Languages

Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMs

Large Language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for h

EricPeter/LLMs-Low-Resourced

## Fine-Tune Alpaca For Any Language In this repository, I've collected all the sources I used to cr

Prompt engineering on large language models (LLMs) in low-resourced language setting

The attached dataset has all the information in regard to Large Language Models (LLMs)

Quantized deep learning models on low-power edge devices for robotic systems

In this work, we present a quantized deep neural network deployed on a low-power edge device, inferr

LLM Probe: Evaluating LLMs for Low-Resource Languages

Despite rapid advances in large language models (LLMs), their linguistic abilities in low-resource a

chinmayjainnnn/LLMs-for-Translation-of-Low-Resource-Languages

Machine translation from assamese to english and vice versa using state of the art LLM's # Hindi-En