Leveraging plant physiological dynamics using physical reservoir computing

• Published in: Scientific reports Vol. 12; no. 1; pp. 12594 - 14
• Main Authors: Pieters, Olivier, De Swaef, Tom, Stock, Michiel, wyffels, Francis
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.07.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/449; 639/705/117; Environmental changes; Environmental conditions; Fragaria ananassa; Humanities and Social Sciences; Information processing; multidisciplinary; Phenotypes; Phenotyping; Physiology; Precision agriculture; Science; Science (multidisciplinary); Transpiration
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-16874-0

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 × 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.