用数字孪生技术实时估算水培生菜生长量并预测未来产量。
A Measurement-Driven Digital Twin Architecture for Plant-Level Biomass Estimation and Growth Forecasting in Hydroponic Systems
- 构建水培系统数字孪生模型，结合传感器数据与神经网络持续更新生长预测。
- 基于1300张RGB-D图像训练的模型，质量估计误差小于1.5克。
- 可提前1至4天预测产量，误差稳定在2克以内，适合智能农业应用。
为应对密集城市中心的食品供应问题，发展了无需土壤的园艺替代方案，如水培系统。本文设计了一种新系统，用于追踪水培环境中单株生菜的生长过程，通过持续输入测量数据和现有模型，动态更新植物生长轨迹的估计。该“数字孪生”模型集成于定制化水培温室中，配备专用水培与传感硬件以实现种植与数据采集。为辅助模型参数更新，采用自研神经网络，以植物的RGB-D图像为输入，对产量进行连续测量。该网络在包含1300张图像的数据集上训练，质量估计误差仅1.5克。集成至定制系统后，数字孪生模型可对未来1至4天的产量做出预测，预测误差维持在约2克。
原文摘要 · Abstract (English)
Alternatives to soil-based horticulture, such as hydroponics, have been developed to respond to food distribution concerns for dense urban centers. A new system was developed to track an individual lettuce plant's growth in a hydroponic environment, utilizing streams of measured information and available models to continuously update the growth trajectory estimates for a plant. These "digital twin" models were integrated into an operating hydroponic greenhouse, with custom horticultural and sensor hardware to grow and measure relevant information. To aid in updating model parameters, plant yield was continuously measured with a custom neural network, using RGB-D images of the plants as an input. The network, trained on a collected dataset of 1300 images, was able to estimate mass within 1.5 g of the ground-truth value. After integration into the custom system, digital twin growth projections could approximate future yield between one and four days in the future, maintaining around a 2 g forecasting error.
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