Abstract
In Bangladesh, many farmers continue to face challenges in accessing timely, expert-level agricultural guidance. This paper presents KrishokBondhu, a voice-enabled, call-centre-integrated advisory platform built on a Retrieval-Augmented Generation (RAG) framework, designed specifically for Bengali-speaking farmers. The system aggregates authoritative agricultural handbooks, extension manuals, and NGO publications; applies Optical Character Recognition (OCR) and document-parsing pipelines to digitize and structure the content; and indexes this corpus in a vector database for efficient semantic retrieval. Through a simple phone-based interface, farmers can call the system to receive real-time, context-aware advice: speech-to-text converts the Bengali query, the RAG module retrieves relevant content, a large language model (Gemma 3-4B) generates a context-grounded response, and text-to-speech delivers the answer in natural spoken Bengali. In a pilot evaluation, KrishokBondhu produced high-quality responses for 72.7% of diverse agricultural queries covering crop management, disease control, and cultivation practices. Compared to the KisanQRS benchmark, the system achieved a composite score of 4.53 (vs. 3.13) on a 5-point scale, a 44.7% improvement, with especially large gains in contextual richness (+367%) and completeness (+100.4%), while maintaining comparable relevance and technical specificity. Semantic similarity analysis further revealed a strong correlation between retrieved context and answer quality, emphasizing the importance of grounding generative responses in curated documentation. KrishokBondhu demonstrates the feasibility of integrating call-centre accessibility, multilingual voice interaction, and modern RAG techniques to deliver expert-level agricultural guidance to remote Bangladeshi farmers, paving the way toward a fully AI-driven agricultural advisory ecosystem.
Abstract (translated)
在孟加拉国，许多农民仍然面临着难以获得及时、专家级别的农业指导的挑战。本文介绍了一种名为KrishokBondhu的声音驱动、呼叫中心集成咨询平台，该平台专为说孟加拉语的农民设计，并基于检索增强生成（RAG）框架构建。系统汇集了权威性的农业手册、推广指南和非政府组织出版物；使用光学字符识别（OCR）和文档解析管道将内容数字化并结构化；并将这些信息存储在一个向量数据库中，以便高效地进行语义检索。通过一个简单的电话界面，农民可以通过拨打该系统来获取实时的上下文感知建议：语音转文本技术会将孟加拉语查询转换为文本，RAG模块检索相关的内容，一个大型语言模型（Gemma 3-4B）生成基于上下文的回答，并以自然口语的形式通过文字转语音技术用孟加拉语传达答案。在试点评估中，KrishokBondhu对涵盖了作物管理、病害控制和栽培实践等多方面农业问题的广泛查询，产生了高质量回答的比例达到了72.7%。与KisanQRS基准系统相比，在5分制评分体系下该系统的综合评分为4.53（而KisanQRS为3.13），得分提高了44.7%，特别是在上下文丰富度（+367%）和完整性（+100.4%）方面有显著提升，同时保持了相当的关联性和技术精确性。语义相似性分析进一步揭示了检索到的内容与答案质量之间的强烈相关性，强调了基于精心整理文档生成回应的重要性。KrishokBondhu展示了将呼叫中心可访问性、多语言语音交互和现代RAG技术整合起来为偏远地区的孟加拉国农民提供专家级农业指导的可行性，并为全由AI驱动的农业咨询生态系统铺平了道路。
URL
https://arxiv.org/abs/2510.18355
