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Leveraging plant physiological dynamics using physical reservoir computing

Olivier Pieters (UGent) , Tom De Swaef (UGent) , Michiel Stock (UGent) and Francis wyffels (UGent)
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Abstract
Plants are complex organisms subject to variable environmental conditions, which influence their physiology and phenotype dynamically. We propose to interpret plants as reservoirs in physical reservoir computing. The physical reservoir computing paradigm originates from computer science; instead of relying on Boolean circuits to perform computations, any substrate that exhibits complex non-linear and temporal dynamics can serve as a computing element. Here, we present the first application of physical reservoir computing with plants. In addition to investigating classical benchmark tasks, we show that Fragaria x ananassa (strawberry) plants can solve environmental and eco-physiological tasks using only eight leaf thickness sensors. Although the results indicate that plants are not suitable for general-purpose computation but are well-suited for eco-physiological tasks such as photosynthetic rate and transpiration rate. Having the means to investigate the information processing by plants improves quantification and understanding of integrative plant responses to dynamic changes in their environment. This first demonstration of physical reservoir computing with plants is key for transitioning towards a holistic view of phenotyping and early stress detection in precision agriculture applications since physical reservoir computing enables us to analyse plant responses in a general way: environmental changes are processed by plants to optimise their phenotype.
Keywords
MEMORY, NETWORKS, PHOTOSYNTHESIS, LEAF, PERFORMANCE, LIGHT

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Citation

Please use this url to cite or link to this publication:

MLA
Pieters, Olivier, et al. “Leveraging Plant Physiological Dynamics Using Physical Reservoir Computing.” SCIENTIFIC REPORTS, vol. 12, no. 1, 2022, doi:10.1038/s41598-022-16874-0.
APA
Pieters, O., De Swaef, T., Stock, M., & wyffels, F. (2022). Leveraging plant physiological dynamics using physical reservoir computing. SCIENTIFIC REPORTS, 12(1). https://doi.org/10.1038/s41598-022-16874-0
Chicago author-date
Pieters, Olivier, Tom De Swaef, Michiel Stock, and Francis wyffels. 2022. “Leveraging Plant Physiological Dynamics Using Physical Reservoir Computing.” SCIENTIFIC REPORTS 12 (1). https://doi.org/10.1038/s41598-022-16874-0.
Chicago author-date (all authors)
Pieters, Olivier, Tom De Swaef, Michiel Stock, and Francis wyffels. 2022. “Leveraging Plant Physiological Dynamics Using Physical Reservoir Computing.” SCIENTIFIC REPORTS 12 (1). doi:10.1038/s41598-022-16874-0.
Vancouver
1.
Pieters O, De Swaef T, Stock M, wyffels F. Leveraging plant physiological dynamics using physical reservoir computing. SCIENTIFIC REPORTS. 2022;12(1).
IEEE
[1]
O. Pieters, T. De Swaef, M. Stock, and F. wyffels, “Leveraging plant physiological dynamics using physical reservoir computing,” SCIENTIFIC REPORTS, vol. 12, no. 1, 2022.
@article{8762047,
  abstract     = {{Plants are complex organisms subject to variable environmental conditions, which influence their physiology and phenotype dynamically. We propose to interpret plants as reservoirs in physical reservoir computing. The physical reservoir computing paradigm originates from computer science; instead of relying on Boolean circuits to perform computations, any substrate that exhibits complex non-linear and temporal dynamics can serve as a computing element. Here, we present the first application of physical reservoir computing with plants. In addition to investigating classical benchmark tasks, we show that Fragaria x ananassa (strawberry) plants can solve environmental and eco-physiological tasks using only eight leaf thickness sensors. Although the results indicate that plants are not suitable for general-purpose computation but are well-suited for eco-physiological tasks such as photosynthetic rate and transpiration rate. Having the means to investigate the information processing by plants improves quantification and understanding of integrative plant responses to dynamic changes in their environment. This first demonstration of physical reservoir computing with plants is key for transitioning towards a holistic view of phenotyping and early stress detection in precision agriculture applications since physical reservoir computing enables us to analyse plant responses in a general way: environmental changes are processed by plants to optimise their phenotype.}},
  articleno    = {{12594}},
  author       = {{Pieters, Olivier and De Swaef, Tom and Stock, Michiel and wyffels, Francis}},
  issn         = {{2045-2322}},
  journal      = {{SCIENTIFIC REPORTS}},
  keywords     = {{MEMORY,NETWORKS,PHOTOSYNTHESIS,LEAF,PERFORMANCE,LIGHT}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{14}},
  title        = {{Leveraging plant physiological dynamics using physical reservoir computing}},
  url          = {{http://dx.doi.org/10.1038/s41598-022-16874-0}},
  volume       = {{12}},
  year         = {{2022}},
}

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