Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Analysis
  • Published:

Artificial intelligence can regulate light and climate systems to reduce energy use in plant factories and support sustainable food production

Nature Food (2024)Cite this article

Subjects

Abstract

Plant factories with artificial lighting (PFALs) can boost food production per unit area but require resources such as carbon dioxide and energy to maintain optimal plant growth conditions. Here we use computational modelling and artificial intelligence (AI) to examine plant–environment interactions across ten diverse global locations with distinct climates. AI reduces energy use by optimizing lighting and climate regulation systems, with energy use in PFALs ranging from 6.42 kWh kg−1 in cooler climates to 7.26 kWh kg−1 in warmer climates, compared to 9.5–10.5 kWh kg−1 in PFALs using existing, non-AI-based technology. Outdoor temperatures between 0 °C and 25 °C favour ventilation-related energy use reduction, with outdoor humidity showing no clear pattern or effect on energy use. Ventilation-related energy savings negatively impact other resource utilization such as carbon dioxide use. AI can substantially enhance energy savings in PFALs and support sustainable food production.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A schematic diagram of a PFAL with DRL for lettuce production.
Fig. 2: Annual comparison of specific energy use between baseline and AI.
Fig. 3: PFAL CO2 consumption of the AI system across the ten cities.
Fig. 4: Annual convective heat and mass transfer between PFAL indoor air and outdoor air.
Fig. 5: Effects of the mean monthly outdoor climate on PFAL energy and CO2 use.
Fig. 6: Cost breakdown for various control activities by the AI system.

Similar content being viewed by others

Data availability

The mean monthly outdoor climate data used in this study are publicly available on timeanddate (https://www.timeanddate.com/weather). The electricity price data for the locations in the United States were obtained from the US Energy Information Administration (https://www.eia.gov/electricity/data/state/); the electricity price data for Reykjavik and Dubai were obtained from Climatescope by BloombergNEF (https://www.global-climatescope.org/markets/is/). All other data are provided in the paper and Supplementary Information. Source data are provided with this paper.

Code availability

All the Python scripts and data necessary to run the analyses presented in this study are publicly available at https://github.com/PEESEgroup/PFAL-DRL.

References

  1. Ruel, M. T., Garrett, J., Yosef, S. & Olivier, M. in Nutrition and Health in a Developing World (eds de Pee, S., Taren, D. & Bloem, M.) 705–735 (Humana Press, 2017).

  2. Béné, C. Resilience of local food systems and links to food security—a review of some important concepts in the context of COVID-19 and other shocks. Food Secur. 12, 805–822 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Chung, M. G. & Liu, J. International food trade benefits biodiversity and food security in low-income countries. Nat. Food 3, 349–355 (2022).

    Article  PubMed  Google Scholar 

  5. Beacham, A. M., Vickers, L. H. & Monaghan, J. M. Vertical farming: a summary of approaches to growing skywards. J. Hortic. Sci. Biotechnol. 94, 277–283 (2019).

    Article  Google Scholar 

  6. Orsini, F., Pennisi, G., Zulfiqar, F. & Gianquinto, G. Sustainable use of resources in plant factories with artificial lighting (PFALs). Eur. J. Hortic. Sci. 85, 297–309 (2020).

    Article  Google Scholar 

  7. Kalantari, F., Tahir, O. M., Joni, R. A. & Fatemi, E. Opportunities and challenges in sustainability of vertical farming: a review. J. Landsc. Ecol. 11, 35–60 (2018).

    Article  Google Scholar 

  8. Gómez, C. et al. Controlled environment food production for urban agriculture. HortScience 54, 1448–1458 (2019).

    Article  Google Scholar 

  9. Lages Barbosa, G. et al. Comparison of land, water, and energy requirements of lettuce grown using hydroponic vs. conventional agricultural methods. Int. J. Environ. Res. Public Health 12, 6879–6891 (2015).

    Article  PubMed Central  Google Scholar 

  10. SharathKumar, M., Heuvelink, E. & Marcelis, L. F. Vertical farming: moving from genetic to environmental modification. Trends Plant Sci. 25, 724–727 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Colhoun, J. Effects of environmental factors on plant disease. Annu. Rev. Phytopathol. 11, 343–364 (1973).

    Article  Google Scholar 

  12. Graamans, L., Baeza, E., Van Den Dobbelsteen, A., Tsafaras, I. & Stanghellini, C. Plant factories versus greenhouses: comparison of resource use efficiency. Agric. Syst. 160, 31–43 (2018).

    Article  Google Scholar 

  13. Weidner, T., Yang, A. & Hamm, M. W. Energy optimisation of plant factories and greenhouses for different climatic conditions. Energy Convers. Manage. 243, 114336 (2021).

    Article  Google Scholar 

  14. Avgoustaki, D. D. & Xydis, G. Indoor vertical farming in the urban nexus context: business growth and resource savings. Sustainability 12, 1965 (2020).

    Article  Google Scholar 

  15. Graamans, L., Tenpierik, M., van den Dobbelsteen, A. & Stanghellini, C. Plant factories: reducing energy demand at high internal heat loads through facade design. Appl. Energy 262, 114544 (2020).

    Article  Google Scholar 

  16. Zhang, Y., Kacira, M. & An, L. A CFD study on improving air flow uniformity in indoor plant factory system. Biosyst. Eng. 147, 193–205 (2016).

    Article  Google Scholar 

  17. Summers, H. M., Sproul, E. & Quinn, J. C. The greenhouse gas emissions of indoor cannabis production in the United States. Nat. Sustain. 4, 644–650 (2021).

    Article  Google Scholar 

  18. Kitaya, Y., Tsuruyama, J., Shibuya, T., Yoshida, M. & Kiyota, M. Effects of air current speed on gas exchange in plant leaves and plant canopies. Adv. Space Res. 31, 177–182 (2003).

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Ashrae, A. Ventilation and Acceptable Indoor Air Quality in Low-rise Residential Buildings (American Society of Heating, Refrigeration and Air-Conditioning Engineers, 2016).

  20. Chinn, R. Y. & Sehulster, L. Guidelines for environmental infection control in health-care facilities: recommendations of CDC and Healthcare Infection Control Practices Advisory Committee (HICPAC). MMWR Recomm. Rep. 52, 1–42 (2003).

  21. Eaton, M., Shelford, T., Cole, M. & Mattson, N. Modeling resource consumption and carbon emissions associated with lettuce production in plant factories. J. Clean. Prod. 384, 135569 (2023).

    Article  CAS  Google Scholar 

  22. Zhang, Y. & Kacira, M. Comparison of energy use efficiency of greenhouse and indoor plant factory system. Eur. J. Hortic. Sci. 85, 310–320 (2020).

    Article  Google Scholar 

  23. Harbick, K. & Albright, L. in VIII International Symposium on Light in Horticulture (eds Currey, C.J. et al.) 1134, 285–292 (ISHS, 2016).

  24. Kozai, T. in Smart Plant Factory: The Next Generation Indoor Vertical Farms (ed. Kozai, T.) 3–13 (Springer, 2018).

  25. Berenguel, M., Yebra, L. J. & Rodríguez, F. in 2003 European control conference (ECC), 2747–2752 (IEEE, 2003).

  26. Van Straten, G. Acceptance of optimal operation and control methods for greenhouse cultivation. Annu. Rev. Control 23, 83–90 (1999).

    Article  Google Scholar 

  27. Kamp, P. G. H. & Timmerman, G. J. Computerised Environmental Control in Greenhouses: A Step by Step Approach. 2nd edn (PTC+, 2003).

  28. Xu, D., Ahmed, H. A., Tong, Y., Yang, Q. & van Willigenburg, L. G. Optimal control as a tool to investigate the profitability of a Chinese plant factory-lettuce production system. Biosyst. Eng. 208, 319–332 (2021).

    Article  CAS  Google Scholar 

  29. Morimoto, T., Torii, T. & Hashimoto, Y. Optimal control of physiological processes of plants in a green plant factory. Control Eng. Pract. 3, 505–511 (1995).

    Article  Google Scholar 

  30. Wang, X. J., Kang, M. Z., Lewlomphaisarl, U., Hua, J. & Wang, H. Y. in 2022 Australian & New Zealand Control Conference (ANZCC) 166–170 (IEEE, 2022).

  31. Van Delden, S. et al. Current status and future challenges in implementing and upscaling vertical farming systems. Nat. Food 2, 944–956 (2021).

    Article  PubMed  Google Scholar 

  32. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 2018).

  33. Baechler, M. C., Gilbride, T. L., Cole, P. C., Hefty, M. G. & Ruiz, K. Guide to Determining Climate Regions by County, Building America Best Practices Series vol. 7.3 (Pacific Northwest National Laboratory, 2015).

  34. Endo, A., Tsurita, I., Burnett, K. & Orencio, P. M. A review of the current state of research on the water, energy, and food nexus. J. Hydrol. Reg. Stud. 11, 20–30 (2017).

    Article  Google Scholar 

  35. Kozai, T. Towards sustainable plant factories with artificial lighting (PFALs) for achieving SDGs. Int. J. Agric. Biol. Eng. 12, 28–37 (2019).

    Google Scholar 

  36. Kozai, T. in Smart Plant Factory: The Next Generation Indoor Vertical Farms (ed. Kozai, T.), 15–29 (Springer, 2018).

  37. Rachmilevitch, S., Cousins, A. B. & Bloom, A. J. Nitrate assimilation in plant shoots depends on photorespiration. Proc. Natl Acad. Sci. USA 101, 11506–11510 (2004).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jackson, W. & Volk, R. Photorespiration. Annu. Rev. Plant Physiol. 21, 385–432 (1970).

    Article  CAS  Google Scholar 

  39. Ogren, W. L. Photorespiration: pathways, regulation, and modification. Annu. Rev. Plant Physiol. 35, 415–442 (1984).

    Article  CAS  Google Scholar 

  40. Weidner, T., Yang, A., Forster, F. & Hamm, M. W. Regional conditions shape the food–energy–land nexus of low-carbon indoor farming. Nat. Food 3, 206–216 (2022).

    Article  CAS  PubMed  Google Scholar 

  41. Gao, L. & Bryan, B. A. Finding pathways to national-scale land-sector sustainability. Nature 544, 217–222 (2017).

    Article  ADS  CAS  PubMed  Google Scholar 

  42. Xie, Z. et al. Conservation opportunities on uncontested lands. Nat. Sustain. 3, 9–15 (2020).

    Article  Google Scholar 

  43. Graham-Rowe, D. Agriculture: beyond food versus fuel. Nature 474, S6–S8 (2011).

    Article  CAS  PubMed  Google Scholar 

  44. Calvert, K. & Mabee, W. More solar farms or more bioenergy crops? Mapping and assessing potential land-use conflicts among renewable energy technologies in eastern Ontario, Canada. Appl. Geogr. 56, 209–221 (2015).

    Article  Google Scholar 

  45. Nie, Y. et al. A food-energy-water nexus approach for land use optimization. Sci. Total Environ. 659, 7–19 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  46. Leung Pah Hang, M. Y., Martinez-Hernandez, E., Leach, M. & Yang, A. Insight-based approach for the design of integrated local food–energy–water systems. Environ. Sci. Technol. 51, 8643–8653 (2017).

    Article  ADS  CAS  PubMed  Google Scholar 

  47. Namany, S., Al-Ansari, T. & Govindan, R. Optimisation of the energy, water, and food nexus for food security scenarios. Comput. Chem. Eng. 129, 106513 (2019).

    Article  CAS  Google Scholar 

  48. Martin, M., Elnour, M. & Siñol, A. C. Environmental life cycle assessment of a large-scale commercial vertical farm. Sustain. Prod. Consum. 40, 182–193 (2023).

    Article  Google Scholar 

  49. Kozai, T., Uraisami, K., Kai, K. & Hayashi, E. in Plant Factory Basics, Applications and Advances (eds Kozai, T., Niu, G. & Masabni, J.) 197–216 (Academic Press, 2022).

  50. Horomia, K. & Gordon-Smith, H. Global CEA Census Report (WayBeyond and Agritecture, 2021).

  51. Van Henten, E. J. Sensitivity analysis of an optimal control problem in greenhouse climate management. Biosyst. Eng. 85, 355–364 (2003).

    Article  Google Scholar 

  52. Chen, W.-H. & You, F. Semiclosed greenhouse climate control under uncertainty via machine learning and data-driven robust model predictive control. IEEE Trans. Control Syst. Technol. 30, 1186–1197 (2021).

    Article  Google Scholar 

  53. Ioslovich, I. Optimal control strategy for greenhouse lettuce: incorporating supplemental lighting. Biosyst Eng 103, 57–67 (2009).

    Article  Google Scholar 

  54. Zelkind, M., Livingston, T. & Verlage, V. in Plant Factory Basics, Applications and Advances (eds Kozai, T., Niu, G. & Masabni, J.) 295–305 (Academic Press, 2022).

  55. Kozai, T. Resource use efficiency of closed plant production system with artificial light: concept, estimation and application to plant factory. Proc. Jpn Acad. B 89, 447–461 (2013).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  56. Voogd, K. L., Allamaa, J. P., Alonso-Mora, J. & Son, T. D. Reinforcement learning from simulation to real world autonomous driving using digital twin. IFAC Pap OnLine 56, 1510–1515 (2023).

    Article  Google Scholar 

  57. Balaji, B. et al. in 2020 IEEE International Conference on Robotics and Automation (ICRA) 2746–2754 (IEEE, 2020).

  58. Van Henten, E. Validation of a dynamic lettuce growth model for greenhouse climate control. Agric. Syst. 45, 55–72 (1994).

    Article  Google Scholar 

  59. Ravishankar, E. et al. Organic solar powered greenhouse performance optimization and global economic opportunity. Energy Environ. Sci. 15, 1659–1671 (2022).

    Article  Google Scholar 

  60. Zhang, Y. & Kacira, M. in Plant Factory Basics, Applications and Advances (eds Kozai, T. et al) 391–400 (Elsevier, 2022).

  61. Ahmed, H. A., Yu-Xin, T. & Qi-Chang, Y. Optimal control of environmental conditions affecting lettuce plant growth in a controlled environment with artificial lighting: a review. S. Afr. J. Bot. 130, 75–89 (2020).

    Article  CAS  Google Scholar 

  62. Zhang, X., He, D., Niu, G., Yan, Z. & Song, J. Effects of environment lighting on the growth, photosynthesis, and quality of hydroponic lettuce in a plant factory. Int. J. Agric. Biol. Eng. 11, 33–40 (2018).

    Google Scholar 

  63. Waycott, W. Photoperiodic response of genetically diverse lettuce accessions. J. Am. Soc. Hortic. Sci. 120, 460–467 (1995).

    Article  Google Scholar 

  64. Van Henten, E. Greenhouse Climate Management: An Optimal Control Approach (Wageningen University and Research, 1994).

  65. Haarnoja, T. et al. Soft actor-critic algorithms and applications. Preprint at arXiv https://doi.org/10.48550/arXiv.1812.05905 (2018).

  66. Meggers, F., Ritter, V., Goffin, P., Baetschmann, M. & Leibundgut, H. Low exergy building systems implementation. Energy 41, 48–55 (2012).

    Article  Google Scholar 

  67. Weng, J. et al. Tianshou: a highly modularized deep reinforcement learning library. J. Mach. Learn. Res. 23, 1–6 (2022).

    ADS  MathSciNet  Google Scholar 

Download references

Acknowledgements

B.D.-N. acknowledges partial support from Schmidt Sciences via an Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship to Cornell University, and the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors gratefully acknowledge the Cornell Institute for Digital Agriculture (CIDA) for partial funding support. This work was partially supported by the Specialty Crop Research Initiative (award no. 2022-51181-38324) from the US Department of Agriculture National Institute of Food and Agriculture.

Author information

Authors and Affiliations

  1. Systems Engineering, Cornell University, Ithaca, NY, USA

    Benjamin Decardi-Nelson & Fengqi You

  2. Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA

    Fengqi You

  3. Cornell Institute for Digital Agriculture, Cornell University, Ithaca, NY, USA

    Fengqi You

Authors
  1. Benjamin Decardi-Nelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fengqi You
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.D.-N. and F.Y. conceived the study. B.D.-N. developed the models. B.D.-N. and F.Y. conducted the study, discussed the results, and wrote and reviewed the paper and Supplementary Information files. F.Y. secured the funding and supervised the research.

Corresponding author

Correspondence to Fengqi You.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Food thanks Michael Martin, Giulia Martini and Aidong Yang for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Outdoor environmental conditions for the various locations considered in this study.

The outdoor CO2 concentration was kept constant at 400 ppm. The first seven plots are arranged according to seven climate zones in the USA. Generally, as we move across the zones, the mean monthly temperature decreases and the number of cold months increases, starting from Miami (Zone 1 A) and ending with Fargo (Zone 7 A). The letters A, B and C denote moist, dry, and marine conditions respectively. a, Miami, Florida (Zone 1 A). b, Phoenix, Arizona (Zone 2B). c, Los Angeles, California (Zone 3B). d, Seattle, Washington (Zone 4 C). e, Chicago, Illinois (Zone 5 A). f, Milwaukee, Wisconsin (Zone 6 A). g, Fargo, North Dakota (Zone 7 A). h, Ithaca, New York (Zone 6 A). i, Reykjavik, Iceland. j, Dubai, United Arab Emirates (UAE).

Source data

Extended Data Fig. 2 Relative annual specific energy use of baseline and AI systems for various control activities.

The values were computed by subtracting the annual specific energy use of the PFAL with the AI system from that of the PFAL with the baseline for each control activity. A positive value indicates energy saving using the AI while negative values indicate that the AI strategy uses more energy than the baseline strategy. a, Relative annual specific energy use for the lighting system. b, Relative annual specific energy use for the cooling/heating system. c, Relative annual specific energy use for the dehumidification system. d, Relative annual specific energy use for the ventilation system. e, Relative annual specific energy use for the CO2 dosing system.

Source data

Extended Data Fig. 3 Trajectories of the PFAL with the AI system for Ithaca, New York in April.

The AI system effectively regulates the environmental factors within the PFAL without negatively affecting crop growth. a, Trajectory of the lettuce dry weight b, Trajectory of the CO2 concentration with elevated CO2 concentration during the light period. c, Trajectory of the air temperature in the PFAL within the operating bounds (short dashes). d, Trajectory of the relative humidity of the air in the PFAL. e, Trajectory of the light power supplied to the artificial lighting system showing a gradual increase in light intensity as the crop matures. f, Trajectory of the supplemental CO2 supply rate. g, Trajectory of the dehumidification rate. h, Trajectory of the cooling/heating rate. i, Trajectory of the ventilation rate characterized by low ventilation during the light period and high ventilation during dark period.

Source data

Extended Data Fig. 4 Trajectories for one day of the PFAL with the AI system for Ithaca, New York in April.

The AI system effectively regulates the environmental factors within the PFAL without negatively affecting crop growth. a, Trajectory of the lettuce dry weight b, Trajectory of the CO2 concentration with elevated CO2 concentration during the light period. c, Trajectory of the air temperature in the PFAL within the operating bounds (short dashes). d, Trajectory of the relative humidity of the air in the PFAL. e, Trajectory of the light power supplied to the artificial lighting system showing a gradual increase in light intensity as the crop matures. f, Trajectory of the supplemental CO2 supply rate. g, Trajectory of the dehumidification rate. h, Trajectory of the cooling/heating rate. i, Trajectory of the ventilation rate characterized by low ventilation during the light period and high ventilation during dark period.

Source data

Extended Data Fig. 5 Relationship between mean monthly outdoor climate and monthly energy or CO2 use of the PFAL.

The plots were generated by combining the monthly data across all the locations under the AI strategy. a, Mean monthly outdoor temperature-outdoor relative humidity-energy use relationship. b, Mean monthly outdoor temperature-outdoor relative humidity-CO2 use relationship. The size of each point represents the magnitude of each data point, and the darker colour means a higher point cloud density.

Source data

Extended Data Fig. 6

DRL policy training procedure.

Extended Data Table 1 Optimum growth conditions for lettuce
Full size table
Extended Data Table 2 SAC hyperparameters
Full size table
Extended Data Table 3 Baseline tuning parameters
Full size table

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Tables 1–3, Methods 1 and 2, and Notes 1–5.

Reporting Summary

Source data

Source Data Fig. 2

Numerical source data for Fig. 2b.

Source Data Fig. 3

Numerical source data for Fig. 3.

Source Data Fig. 4

Numerical source data for Fig. 4.

Source Data Fig. 5 and Extended Data Fig. 5

Numerical source data for Fig. 5 and Extended Data Fig. 5.

Source Data Fig. 6

Numerical source data for Fig. 6.

Source Data Extended Data Fig. 1

Numerical source data for Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Numerical source data for Extended Data Fig. 2.

Source Data Extended Data Figs. 3 and 4

Numerical source data for Extended Data Fig. 3 and Extended Data Fig. 4.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Decardi-Nelson, B., You, F. Artificial intelligence can regulate light and climate systems to reduce energy use in plant factories and support sustainable food production. Nat Food (2024). https://doi.org/10.1038/s43016-024-01045-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s43016-024-01045-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics