KrishokBondhu: A Retrieval-Augmented Voice-Based Agricultural Advisory Call Center for Bengali Farmers

• URL: http://arxiv.org/abs/2510.18355v1
• Date: Tue, 21 Oct 2025 07:24:55 GMT
• Title: KrishokBondhu: A Retrieval-Augmented Voice-Based Agricultural Advisory Call Center for Bengali Farmers
• Abstract summary: In Bangladesh, many farmers continue to face challenges in accessing timely, expert-level agricultural guidance.<n>This paper presents KrishokBondhu, a voice-enabled, call-centre-integrated advisory platform built on a Retrieval-Augmented Generation framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Benchmarking Automatic Speech Recognition for Indian Languages in Agricultural Contexts [0.0]
  The digitization of agricultural advisory services in India requires robust Automatic Speech Recognition systems.<n>This paper presents a benchmarking framework for evaluating ASR performance in agricultural contexts across Hindi, Telugu, and Odia languages.
  arXiv  Detail & Related papers  (2026-01-31T15:04:21Z)
• Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory [0.0]
  Access to reliable agricultural advisory remains limited in many developing regions due to a persistent language barrier.<n>This paper presents a cost-efficient, cross-lingual Retrieval-Augmented Generation framework for Bengali agricultural advisory.
  arXiv  Detail & Related papers  (2026-01-05T12:41:44Z)
• A Multimodal Conversational Assistant for the Characterization of Agricultural Plots from Geospatial Open Data [0.0]
  This study presents an open-source conversational assistant that integrates multimodal retrieval and large language models (LLMs)<n>The proposed architecture combines orthophotos, Sentinel-2 vegetation indices, and user-provided documents through retrieval-augmented generation (RAG)<n>Preliminary results show that the system is capable of generating clear, relevant, and context-aware responses to agricultural queries.
  arXiv  Detail & Related papers  (2025-09-22T09:02:53Z)
• AgriDoctor: A Multimodal Intelligent Assistant for Agriculture [45.77373971125537]
  AgriDoctor is a modular and multimodal framework designed for intelligent crop disease diagnosis and agricultural knowledge interaction.<n>To facilitate effective training and evaluation, we construct AgriMM, a benchmark comprising 400000 annotated disease images, 831 expert-curated knowledge entries, and 300000 bilingual prompts for intent-driven tool selection.<n>Experiments demonstrate that AgriDoctor, trained on AgriMM, significantly outperforms state-of-the-art LVLMs on fine-grained agricultural tasks.
  arXiv  Detail & Related papers  (2025-09-21T11:51:57Z)
• HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis [55.2480439325792]
  HySemRAG is a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG)<n>System addresses limitations in existing RAG architectures through a multi-layered approach.
  arXiv  Detail & Related papers  (2025-08-01T20:30:42Z)
• Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering [17.122839125789557]
  Indian farmers often lack timely, accessible, and language-friendly agricultural advice, especially in rural areas with low literacy.<n>This paper presents a novel AI-powered agricultural chatbots, Krishi Sathi, designed to support Indian farmers by providing personalized, easy-to-understand answers to their queries through both text and speech.<n>Krishi Sathi follows a structured, multi-turn conversation flow to gradually collect the necessary details from the farmer, ensuring the query is fully understood before generating a response.
  arXiv  Detail & Related papers  (2025-07-28T09:00:44Z)
• AgriCHN: A Comprehensive Cross-domain Resource for Chinese Agricultural Named Entity Recognition [30.51577375197722]
  We present AgriCHN, a comprehensive open-source Chinese resource designed to promote the accuracy of automated agricultural entity annotation.<n>The dataset has been meticulously curated from a wealth of agricultural articles, comprising a total of 4,040 sentences and encapsulating 15,799 agricultural entity mentions.<n>A benchmark task has also been constructed using several state-of-the-art neural NER models.
  arXiv  Detail & Related papers  (2025-06-21T04:21:11Z)
• AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application [1.9643850583333375]
  AgroLLM is designed to enhance knowledge-sharing and education in agriculture using Large Language Models (LLMs) and a Retrieval-Augmented Generation (RAG) framework.<n>A comparative study of three advanced models was conducted to evaluate performance across four key agricultural domains.<n>ChatGPT-4o Mini with RAG achieved the highest accuracy at 93%.
  arXiv  Detail & Related papers  (2025-02-28T04:13:18Z)
• Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts [48.89527378273811]
  This paper explores integrating Automatic Speech Recognition into natural language query systems for Korean meteorologists.<n>We address challenges in developing ASR systems for the Korean weather domain, specifically specialized vocabulary and Korean linguistic intricacies.
  arXiv  Detail & Related papers  (2024-10-24T05:40:07Z)
• Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues [66.69453609603875]
  Sociocultural norms serve as guiding principles for personal conduct in social interactions. We propose a scalable approach for constructing a Sociocultural Norm (SCN) Base using Large Language Models (LLMs) We construct a comprehensive and publicly accessible Chinese Sociocultural NormBase.
  arXiv  Detail & Related papers  (2024-10-04T00:08:46Z)
• FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems [53.89014188309486]
  We present a flexible neural framework that can integrate contextual information from multiple channels. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
  arXiv  Detail & Related papers  (2023-03-31T23:58:28Z)
• RHO ($\rho$): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding [57.46495388734495]
  This paper presents RHO ($rho$) utilizing the representations of linked entities and relation predicates from a knowledge graph (KG) We propose (1) local knowledge grounding to combine textual embeddings with the corresponding KG embeddings; and (2) global knowledge grounding to equip RHO with multi-hop reasoning abilities via the attention mechanism.
  arXiv  Detail & Related papers  (2022-12-03T10:36:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.