Closed-Loop LLM Co-Pilots for Digital Agriculture (arXiv:2608.09949)
论文提出LLM闭环控制农业系统，显著节能并缩短生产周期，方法新颖，数据详实，但发布于近两个月前，时效性较低。
Serge Kernbach 发表论文。本研究评估大语言模型(LLM)在复杂生物系统中的应用,从数据分析演进到AI引导的自主实验。框架由49通道植物传感器网络数据驱动,涵盖多光谱、电化学和介电模态。在最少时间模式下,系统将生产周期缩短35%;在能源优化模式下,通过利用光脉冲的生理惯性,降低能源消耗18%。最后,代理自主开发了黑暗诱导叶绿素积累策略,实现67.9%的节能。本框架将LLM转变为数字农业的自主副驾驶,改善成本-价值比,降低计算和专家劳动力约束。
Abstract:This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving from data analysis to autonomous, AI-guided experimentation. The framework is driven by data from a 49-channel phytosensor network, encompassing multispectral, electrochemical, and dielectric modalities. To enhance accessibility, the system provides real-time natural-language interpretation for both specialists and non-experts. However, its core advantage lies in the transition from human-in-the-loop analysis to autonomous control. Processing biophysical data, the LLM evaluates plant physiology and triggers hardware actuators to optimize microclimates, execute phenotyping protocols, or induce controlled stress scenarios. This closed-loop architecture establishes a direct AI-biology interface, enabling data-driven exploration of complex biosystems and ecologies. The framework was validated across three case studies, based on a vertical farm and a single-plant setup and deciphered complex micro- and macro-fluctuations in plant physiology. Agents in a production-scale deployment executed multi-parameter optimization, balancing biomass accumulation, chlorophyll content, and energy consumption. The LLM processed biosensing telemetry to modulate full-spectrum, 450 nm, and 660 nm lighting at 2-hour intervals. Compared to periodic control, the system in minimal-time mode reduced the production cycle by 35%. In the energy-optimization mode, it reduced energy consumption by 18% with only a marginal increase in cultivation time, exploiting physiological inertia via light pulses. Finally, the agents autonomously developed an unforeseen strategy of dark-induced chlorophyll accumulation, resulting in a 67.9% energy saving. This framework transforms LLMs into autonomous co-pilots for digital agriculture, improving the cost-to-value ratio and lowering computational and expert-labor constraints.
Subjects:Artificial Intelligence (cs.AI); Biological Physics (physics.bio-ph)
Cite as:arXiv:2608.09949 [cs.AI]
(or arXiv:2608.09949v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.09949
arXiv-issued DOI via DataCite
Submission history
From: Serge Kernbach [view email]
[v1] Thu, 9 Jul 2026 11:05:42 UTC (7,699 KB)