Munser Lukas, Sathyanarayanan Kiran Kumar, Raecke Jonathan, Mansour Mohamed Mokhtar, Uland Morgan Emily, Streif Stefan
Professorship Automatic Control and System Dynamics, Technische Universität Chemnitz, Reichenhainer Straße 70, 09126 Chemnitz, Germany.
Department of Bioresources, Fraunhofer Institute for Molecular Biology and Applied Ecology, Ohlebergsweg 12, 35392 Gießen, Germany.
Sensors (Basel). 2025 Aug 2;25(15):4770. doi: 10.3390/s25154770.
Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems.
连续且准确的生物量测量是现代植物生产系统中实现控制、决策和优化的关键因素。它支持开发用于先进控制策略（如模型预测控制）的植物生长模型，并实现响应式、数据驱动且依赖植物状态的栽培。传统的生物量测量方法，如破坏性采样，既耗时又不适用于高频监测。相比之下，使用计算机视觉和深度学习的基于图像的估计需要频繁重新训练，并且对光照或植物形态的变化很敏感。这项工作介绍了一种专为垂直农场应用量身定制的低成本、基于称重传感器的生物量监测系统。该系统在单个种植托盘层面运行，在不切实际的植物层面传感和过于粗略的货架层面测量之间提供了一个有价值的中间地带。托盘层面的数据允许进行局部控制操作，例如调整每个托盘的光谱和强度，从而提高可控LED系统的效用。这种粒度支持特定层的优化和异常检测，而这在货架层面反馈中是不可行的。生物量传感器易于扩展且可进行改装，能够解决诸如机械噪声和热漂移等常见挑战。它为动态生长环境中的生物量监测提供了一种实用且强大的解决方案，能够在商业和面向研究的垂直农场系统中实现更精细的控制和更明智的决策。所开发的传感器针对人工收获数据进行了测试和验证，与实际植物生物量高度一致，并证实了其适用于集成到垂直农场系统中。
