[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2414":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":56},2414,"AgroVaani: Vernacular Generative Intelligence for Zero-Barrier Precision Agriculture Advisory Using Multilingual RAG-LLMs","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fijirt.208380-459","Farming today is becoming more challenging because of unpredictable weather, worsening soil health, more frequent pest and disease outbreaks, and limited access to expert advice when farmers need it most.While traditional agricultural advisory services have helped farmers for many years, they often fall short in giving advice that suits each farmer's unique location and situation.This is due to not having enough experts, language differences, and varied farming practices across regions.To ad-dress these issues, this research introduces a Smart Farming Assistant Chatbot powered by advanced Al technologies like Generative Al, Large Language Models, Natural Language Processing, and Retrieval-Augmented Generation.This chatbot is designed to be multilingual and voice-enabled, so farmers can easily talk to it in their own language or dialect.It supports many regional languages including Marathi, Hindi, Kan-nada, Tamil, Telugu, Bengali, Gujarati, Punjabi, Malayalam, Odia, Assamese, and English.Unlike typical chatbots that use fixed answers, this assistant understands natural conversations, local farming terms, and even mixed language use like Marathi-English or Hindi-English, making communication smooth and natural.The system brings together a wide range of farming information-from details about crops, soil, and weather, to pest control, fertilizers, market trends, and government schemes.By combining the power of Al with trustworthy agricultural knowledge, the chatbot offers practical, ac-curate, and personalized advice while minimizing the chances of giving wrong information.What makes this system truly special is that it breaks down language barriers, allowing farmers to get expert-level help without needing technical skills or knowing a specific language.Farmers can interact with it through text or voice, getting support for everything from choosing crops and planning irrigation, to managing soil, preventing diseases, and making sustainable farming decisions.This approach aims to empower small and marginal farmers, reduce their reliance on traditional advisory ser-vices, help them use resources more efficiently, and promote smart, inclusive farming that benefits everyone.Ultimately, this system moves us closer to an Al-driven agricultural world where technology adapts to farmers' needs, making farming easier and more productive for all.","当今农业面临着日益严峻的挑战，包括不可预测的天气、不断恶化的土壤健康、愈发频繁的病虫害暴发，以及农民在最需要时难以获得专家指导。尽管传统农业咨询服务多年来为农民提供了帮助，但往往难以给出适合每位农民特定地理位置和实际情况的建议。这源于专家数量不足、语言差异以及各地区耕作方式的不同。为解决这些问题，本研究提出了一款智能农业助手聊天机器人，由生成式人工智能（Generative AI）、大语言模型（Large Language Models）、自然语言处理（Natural Language Processing）和检索增强生成（Retrieval-Augmented Generation）等先进人工智能技术驱动。该聊天机器人设计为多语言和语音交互模式，农民可以用自己的语言或方言轻松与之对话。它支持多种区域语言，包括马拉地语、印地语、卡纳达语、泰米尔语、泰卢固语、孟加拉语、古吉拉特语、旁遮普语、马拉雅拉姆语、奥里亚语、阿萨姆语和英语。与使用固定答案的典型聊天机器人不同，该助手能够理解自然对话、地方农业术语，甚至马拉地语-英语或印地语-英语等混合语言使用，使交流顺畅自然。该系统汇集了广泛的农业信息——从作物、土壤和天气的详细信息，到病虫害防治、肥料、市场趋势和政府计划。通过将人工智能的能力与可靠的农业知识相结合，该聊天机器人提供实用、准确且个性化的建议，同时最大限度地降低给出错误信息的可能性。该系统的真正特别之处在于它打破了语言障碍，使农民无需技术技能或掌握特定语言即可获得专家级的帮助。农民可以通过文字或语音与之互动，获得从选择作物、规划灌溉，到管理土壤、预防疾病和做出可持续农业决策等各方面的支持。该方法旨在赋能小农户和边缘农户，减少他们对传统咨询服务的依赖，帮助他们更高效地利用资源，并促进惠及所有人的智能、包容性农业。最终，该系统推动我们更接近一个人工智能驱动的农业世界，在那里技术适应农民的需求，使农业对所有人来说都更轻松、更高效。",null,"International Journal of Innovative Research in Technology","2026-09-11T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,6,8,1,"面向印度多语种农户的RAG-LLM语音农业问答系统，方法组合与多语言覆盖有实质增量，但期刊权威性一般，属细分领域技术进展。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","多语言大模型","农业问答机器人","小农户服务",0,"10.64643\u002Fijirt.208380-459",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":47,"card":48,"direction":54,"ingested_from":55},"W7212237513",[37,39,41,43,45],{"name":38,"orcid":9},"Devansh Adik Wable",{"name":40,"orcid":9},"Sahil Balshiram Pawar",{"name":42,"orcid":9},"Shravani Chetan Kalid",{"name":44,"orcid":9},"Sanjay Nathu Bombale",{"name":46,"orcid":9},"Mayuri Jayesh Patil -","https:\u002F\u002Fijirt.org\u002Fpublishedpaper\u002FIJIRT208380_PAPER.pdf",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"构建多语言语音农业咨询聊天机器人，用RAG-LLM为农民提供零障碍精准建议。","生成式AI、大语言模型、NLP与检索增强生成，支持12种印度语言及混合语。","系统能理解本地农耕术语与混合语言，提供个性化、可信的农业建议。","数字乡村与农业信息化","可探索低资源方言的RAG知识库构建与幻觉抑制，并评估对农户决策的实际影响。","智慧农业 \u002F 农业物联网","openalex","2026-09-14T23:30:09.870436Z"]