Deploying and Evaluating a Smart-Agriculture Agentic Engine for Full-Season Soybean Farm Operations

📅 2026-08-31
📈 Citations: 0
Influential: 0
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🤖 AI Summary
本文介绍了FAIRY系统,用于解决大豆农场全季节运营问题,通过整合多种农业技术并采用事件驱动模式执行和评估农艺操作。
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

Smart-Agriculture
Agentic Operations
Soybean Farm
Spatiotemporal Workflows
Agronomic Constraints
Innovation

Methods, ideas, or system contributions that make the work stand out.

everything is an event
agricultural agent system
spatiotemporal workflows
event-driven world model
full-stack
Ao Qu
Ao Qu
Massachusetts Institute of Technology
Language AgentMultisensory AIComputational Social Science
P
Panagiotis Michelakis
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
L
Linyuan Han
Faculty of Computing, Harbin Institute of Technology, State Key Laboratory of Smart Farm Technologies and Systems, Harbin, China
Y
Yiannis Hadjiyianni
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Kun Ouyang
Kun Ouyang
National University of Singapore
human mobilitymachine learning
K
Konstantinos Siskos
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Feng Li
Feng Li
Professor, School of Computer Science and Technology, Shandong University, China
Distributed Algorithms and SystemsMachine LearningEdge ComputingInternet of Things
Ran Meng
Ran Meng
Faculty of Computing, Harbin Institute of Technology, State Key Laboratory of Smart Farm Technologies and Systems, Harbin, China
Jingchi Jiang
Jingchi Jiang
Harbin Institute of Technology
Knowledge GraphMachine LearningData Mining
Dimitrios Stamoulis
Dimitrios Stamoulis
Harbin Institute of Technology
Agentic AIGeospatial AIComputer VisionHardware systems
Jie Liu
Jie Liu
Harbin Institute of Technology
Computer Science and Engineering