{"slug": "deploying-and-evaluating-a-smart-agriculture-agentic-engine-for-full-season-farm", "title": "Deploying and Evaluating a Smart-Agriculture Agentic Engine for Full-Season Soybean Farm Operations", "summary": "Researchers at Harbin Institute of Technology developed and deployed FAIRY, a full-stack smart-agriculture agent system, on an operating soybean research farm to execute and evaluate agentic agronomic operations across full-season workflows. The system integrates APIs and infrastructure for machinery, sensors, drones, satellite data, weather, and crop models, and was used to evaluate nine state-of-the-art agent controllers across one hundred full-season soybean scenarios, measuring agentic success, spatiotemporal correctness, token cost, and edge-device runtime.", "body_md": "arXiv:2609.00106v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/deploying-and-evaluating-a-smart-agriculture-agentic-engine-for-full-season-farm", "canonical_source": "https://arxiv.org/abs/2609.00106", "published_at": "2026-09-02 04:00:00+00:00", "updated_at": "2026-09-02 04:26:42.752166+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-research"], "entities": ["Harbin Institute of Technology", "FAIRY"], "alternates": {"html": "https://wpnews.pro/news/deploying-and-evaluating-a-smart-agriculture-agentic-engine-for-full-season-farm", "markdown": "https://wpnews.pro/news/deploying-and-evaluating-a-smart-agriculture-agentic-engine-for-full-season-farm.md", "text": "https://wpnews.pro/news/deploying-and-evaluating-a-smart-agriculture-agentic-engine-for-full-season-farm.txt", "jsonld": "https://wpnews.pro/news/deploying-and-evaluating-a-smart-agriculture-agentic-engine-for-full-season-farm.jsonld"}}