用聊天机器人把农田传感器数据变成农民能懂的种地建议。
Kissan-Dost: Bridging the Last Mile in Smallholder Precision Agriculture with Conversational IoT
- 通过对话系统将土壤和天气数据转化为口语化指导。
- 99个作物问题回答正确率超90%，响应速度低于1秒。
- 适合农业物联网落地难的小农户，尤其关注最后一公里应用。
我们提出 Kissan-Dost，一个支持多语言、基于传感器的对话式系统，将实时农田测量与天气数据转化为通过 WhatsApp 文本或语音传递的通俗指导。该系统结合农作物土壤与气候传感器，采用检索增强生成技术，并通过模块化流程确保信息的可追溯性与主动预警。在为期90天、两个地点、五名参与者的试点中，分三个阶段（基线、仅仪表盘、仅聊天机器人）进行测试。仪表盘使用零星且迅速衰减，而聊天机器人几乎每日使用，并促成具体农事决策。对99个基于传感器的作物查询进行控制测试，实现超过90%的准确率，端到端延迟小于1秒，同时输出高质量翻译结果。结果表明，真正释放现有农业物联网潜力的关键在于精心设计的“最后一公里”集成，而非新型硬件。
原文摘要 · Abstract (English)
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.
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