Indoor Farming Research


We aim to develop digital-twin and machine learning algorithms for addressing key aspects of LED driven pod-based hydroponic farming, resource use efficiency, and related plant pathogen challenges. We are motivated by recent products which are presented without much analysis or optimization so that their efficacy is unclear. Our group is expected to lead to more integrative AI large data for supporting the development of the hydroponic industry. This project’s focus on sensors and data gathering will also translate to tools that may be used in, e.g., greenhouses.

Why this work is important:

  • Less water usage
  • Less pesticide usage
  • Improved yield
  • More consistent growth/product (year-round cultivation)
  • Reduce the impact from the local climate/ climate change

This work is supported by the USDA/NSF AI Institute for Next Generation Food Systems (AIFS) through the AFRI Competitive Grant no. 2020-67021-32855/project accession no. 1024262 from the USDA National Institute of Food and Agriculture.


Optimizing High Energy Light Sterilization in Controlled Environment Agriculture

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Composition Characterization Using Multispectral Photon Counting

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Optimizing Energy Management Systems for Agricultural Microgrids

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A Digital Twin Framework for Agrophotovoltaic Systems

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Robust and Viable Framework for Uncertainty Quantification in Food Systems

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Farming Growth Towers Design of Vertical Aeroponic Systems

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Simulator for Autonomous Drone Flight and Data Collection in Agricultural Environments

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3D Printing Bio-Structures to Improve Growth of Various LED-Grown Mycelia

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Publications from the group

Zohdi, T. I. (2023) A machine-learning digital-twin for rapid large-scale solar-thermal energy system design, Computer Methods in Applied Mechanics and Engineering, 115991,

Mengi, E., Samara, O.A., and Zohdi, T. I. (2023) Crop-driven optimization of agrivoltaics using a digital-replica framework. Smart Agricultural Technology. Volume 4,

Zheng, T., Bujarbaruah, M., Stürz, Y.R., & Borrelli, F. (2023) Safe Human-Robot Collaborative Transportation via Trust-Driven Role Adaptation. IEEE American Control Conference (ACC) 

Isied, R. Mengi, E. and Zohdi, T. I. (2022) A digital twin framework for genomic-based optimization of an agrophotovoltaic greenhouse system. Proceeding of the Royal Society A. Volume 478, Issue 2267,

Goodrich, P., Betancourt, O., Arias, A., and Zohdi, T. I. (2022) Placement and drone flight path mapping of agricultural soil sensors using machine learning. Computers and Electronics in Agriculture.

X. Wu, S. Chen, K. Sreenath, and M. W. Mueller (2022) Perception-aware receding horizon trajectory planning for multicopters with visual-inertial odometry, IEEE Access, Vol 10, pp. 87911-87922

X. Wu, J. Zeng, A. Tagliabue, and M.W. Mueller (2022) Model-free online motion adaptation for energy efficient flights of multicopters, IEEE Access, Vol 10, pp. 65507 – 65519

Zohdi, T. I. (2022) A Note on Rapid Genetic Calibration of Artificial Neural Networks. Computational Mechanics.

Zohdi, T. I. (2022) An adaptive digital framework for energy management of complex multi-device systems. Computational Mechanics.

Zohdi, T. I. (2022) Machine-learning and Digital-Twins for Rapid Evaluation and Design of Injected Vaccine Immune Responses. Computer Methods Appl. Mech. Eng.

Zohdi, T. I. (2022). A digital-twin and machine-learning framework for precise heat and energy management of data-centers. Computational Mechanics. 

Ilias Tagkopoulos, Stephen F. Brown, Xin Liu, Qing Zhao, Tarek I. Zohdi, J. Mason Earles, Nitin Nitin, Daniel E. Runcie, Danielle G. Lemay, Aaron D. Smith, Pamela C. Ronald, Hao Feng, Gabriel David Youtsey. (2022) Special report: AI Institute for next generation food systems (AIFS) Computers and Electronics in Agriculture. 

Zohdi, T. I. (2021) A Digital-Twin and Machine-learning Framework for the Design of Multiobjective Agrophotovoltaic Solar Farms, Computational Mechanics.

Zohdi, T. I. (2021) A Digital-Twin and Machine-learning Framework for Ventilation System Optimization for Capturing Infectious Disease Respiratory Emissions, Archives of Computational Methods in Engineering.