发表机构
synkrasis-labs（辛克拉西斯实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发并部署了智能农业智能体系统FAIRY，在100个全季大豆场景中评估9种最先进智能体控制器，结合多维度指标建立评估套件，用于全季大豆农场运营的智能体引擎部署与评估。
AI 中文摘要
本文介绍了FAIRY，这是一个全栈智能农业智能体系统，专为哈尔滨工业大学智能农业站点的一个运营中的大豆研究农场开发并部署。我们开发FAIRY是为了在跨越起垄准备、播种、灌溉、施肥、病虫害处理、收获、谷物处理、干燥和储存的全季时空工作流上执行和评估智能体农艺操作。FAIRY整合了生产级机械、固定土壤和冠层传感器、多光谱与热无人机、卫星植被产品、气象站、校准作物过程模型、农艺记录以及多季产量历史的API和基础设施。该系统围绕新颖的“一切皆为事件”执行范式构建，该范式将时空世界演化、遥感与无人机观测、传感器读数、作物生长转变、机械动作和管理干预表示为共享农场过程引擎中的状态变化事件。在这个事件驱动的世界模型之上，FAIRY实现了完整的智能体栈：原子农艺技能的知识库、多智能体控制器与编排后端、前沿模型与边缘模型执行、全路径跟踪日志以及本地节点上的部署分析。我们使用FAIRY在100个全季大豆场景中评估了9种最先进的智能体控制器，这些场景保留了64垄田地的空间观测、时间决策序列、农艺约束、延迟效应与最终产量之间的操作耦合。我们开发了一套评估套件，结合了智能体成功率、全路径时空正确性、令牌成本和边缘设备运行时间。
英文摘要
This paper presents FAIRY, a full-stack smart-agriculture agent system developed for and deployed to an operating soybean research farm at Harbin Institute of Technology's smart-agriculture site. We develop FAIRY to execute and evaluate agentic agronomic operations on full-season spatiotemporal workflows that span ridge preparation, planting, irrigation, fertilization, pest and disease treatment, harvest, grain handling, drying, and storage. FAIRY integrates APIs and infrastructure across production-grade machinery, fixed soil and canopy sensors, multispectral and thermal drones, satellite vegetation products, a weather station, calibrated crop-process models, agronomic records, and multi-season yield histories. The system is built around the novel "everything is an event" execution paradigm, which represents spatiotemporal world evolution, remote sensing and UAV observations, sensor readings, crop-growth transitions, machinery actions, and management interventions as state-changing events in a shared farm process engine. On top of this event-driven world model, FAIRY implements a complete agentic stack: a knowledge library of atomic agronomic skills; multi-agent controller and orchestration backends; frontier- and edge-model execution; full-path trace logging; and deployment profiling on local nodes. We use FAIRY to evaluate nine state-of-the-art agent controllers across one hundred full-season soybean scenarios that preserve the operational coupling between spatial observations in a 64-ridge field, temporal decision sequences, agronomic constraints, delayed effects, and final yield. We develop an evaluation suite that combines agentic success, full-path spatiotemporal correctness, token cost, and edge-device runtime.
CommentsAccepted to ACM SIGSPATIAL 2026
