Kissan-Dost：通过对话物联网弥合小农户精准农业的最后一公里
Kissan-Dost: Bridging the Last Mile in Smallholder Precision Agriculture with Conversational IoT
AI总结：
Kissan-Dost通过对话物联网系统，利用传感器数据和多语言翻译，为小农户提供精准农业的实时指导和高效决策支持。
摘要：
我们提出了Kissan-Dost，一个多语言、基于传感器的对话系统，将实时农场测量和天气转化为普通语言指导，通过WhatsApp文本或语音传递。该系统结合了商品土壤和气候传感器与检索增强生成，然后通过模块化流程强制实施接地、可追溯性和主动警报。在为期90天、两个地点的试点中，有五名参与者参与了三个阶段（基线、仅仪表盘、仅聊天机器人）。仪表盘参与度稀疏且逐渐消失，而聊天机器人几乎每天都被使用，并指导了具体的行动。对99个传感器接地作物查询的受控测试实现了超过90%的正确率，同时具有亚秒级端到端延迟，以及高质量的翻译输出。结果表明，仔细的最后一公里整合，而不是新的电路，解锁了现有农业物联网对小农户的潜在价值。
英文摘要：
We present Kissan-Dost, a multilingual, sensor-grounded conversational system that turns live on-farm measurements and weather into plain-language guidance delivered over WhatsApp text or voice. The system couples commodity soil and climate sensors with retrieval-augmented generation, then enforces grounding, traceability, and proactive alerts through a modular pipeline. In a 90-day, two-site pilot with five participants, we ran three phases (baseline, dashboard only, chatbot only). Dashboard engagement was sporadic and faded, while the chatbot was used nearly daily and informed concrete actions. Controlled tests on 99 sensor-grounded crop queries achieved over 90 percent correctness with subsecond end-to-end latency, alongside high-quality translation outputs. Results show that careful last-mile integration, not novel circuitry, unlocks the latent value of existing Agri-IoT for smallholders.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
