发表机构
CYBRES GmbH（CYBRES有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发闭环LLM协作副驾驶系统，基于49通道植物传感器网络实现数字农业自主控制，通过三案例验证可缩短生产周期、降低能耗，提升成本价值比。
AI 中文摘要
本研究评估大语言模型（LLM）在复杂生物系统中的应用，其范畴从数据分析延伸至自主的AI引导实验。该框架由49通道植物传感器网络的数据驱动，涵盖多光谱、电化学和介电模态。为提升可访问性，系统为专家和非专业人员提供实时自然语言解读，但其核心优势在于从人在回路分析向自主控制的转变。LLM处理生物物理数据后，评估植物生理状况并触发硬件执行器，以优化微环境、执行表型分析方案或诱导可控胁迫场景。这种闭环架构建立了直接的AI-生物界面，支持对复杂生物系统和生态系统的数据驱动探索。该框架通过三个案例研究进行验证，案例基于垂直农场和单植株装置，可解析植物生理的复杂微观和宏观波动。生产规模部署中的智能体执行多参数优化，平衡生物量积累、叶绿素含量与能耗。LLM每2小时处理生物传感遥测数据，以调节全光谱、450nm及660nm光照。与周期性控制相比，该系统在最短时间模式下将生产周期缩短35%；在能耗优化模式下，利用生理惯性通过光脉冲将能耗降低18%，仅伴随 cultivation时间的小幅增加；最后，智能体自主开发出未预见的黑暗诱导叶绿素积累策略，实现67.9%的能耗节省。该框架将LLM转变为数字农业的自主协作副驾驶，提升了成本价值比，降低了计算和专家人力约束。
英文摘要
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.
