TY - JOUR
T1 - Plant science in the age of simulation intelligence
AU - Stock, Michiel
AU - Pieters, Olivier
AU - De Swaef, Tom
AU - wyffels, Francis
N1 - Copyright © 2024 Stock, Pieters, De Swaef and wyffels.
PY - 2024/1/16
Y1 - 2024/1/16
N2 - Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as “simulation intelligence”, has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.
AB - Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as “simulation intelligence”, has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.
KW - artificial intelligence
KW - digital agriculture
KW - digital twin
KW - modeling
KW - phenotyping
KW - quantified plant
KW - scientific computing
KW - simulation intelligence
UR - https://www.mendeley.com/catalogue/1923bc06-4c9b-3013-a273-ba589bb38264/
U2 - 10.3389/fpls.2023.1299208
DO - 10.3389/fpls.2023.1299208
M3 - Article
C2 - 38293629
SN - 1664-462X
VL - 14
JO - Frontiers in Plant Science
JF - Frontiers in Plant Science
ER -