A Measurement-Driven Digital Twin Architecture for Plant-Level Biomass Estimation and Growth Forecasting in Hydroponic Systems
Researchers develop a digital twin system for hydroponic lettuce, using sensors and neural networks to track individual plant growth and predict yield. The custom neural network estimates mass within 1.5 g from RGB-D images, and the integrated system forecasts yield 1-4 days ahead with ~2 g error.
[2606.02796] A Measurement-Driven Digital Twin Architecture for Plant-Level Biomass Estimation and Growth Forecasting in Hydroponic Systems
[Submitted on 1 Jun 2026]
Title:A Measurement-Driven Digital Twin Architecture for Plant-Level Biomass Estimation and Growth Forecasting in Hydroponic Systems
View a PDF of the paper titled A Measurement-Driven Digital Twin Architecture for Plant-Level Biomass Estimation and Growth Forecasting in Hydroponic Systems, by Morgan Mayborne and 2 other authors
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Abstract: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.
Comments: 7 pages, 6 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.02796 [cs.RO]
(or arXiv:2606.02796v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.02796
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Abhisesh Silwal [view email] [v1] Mon, 1 Jun 2026 19:00:24 UTC (2,430 KB)
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