Advancing Plant Intelligence (PI) in parallel with AI: Proposal of plant Intelligence Quotient (pIQ) as a germinating point
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PUBLIC SUMMARY
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Plant Intelligence (PI) emerges as a potential branch parallel to artificial intelligence (AI).
Mechanism complexity is the bottleneck but the source of promoting this promising field.
Challenges in assessing PI prevail, e.g., is tree intelligence a higher or lower level of PI?
Testing do-math is proposed for the quantification of plant Intelligence Quotient (pIQ).
Advancing PI will drive paradigm shifts and may even stimulate disciplinary innovations.
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ABSTRACT
Parallel to advances in artificial intelligence (AI), research on alternative forms of bionic intelligence has received growing emphasis as a means to broaden the science of intelligence. Retracing the origins of AI suggests that plant intelligence (PI) might be a promising research direction, particularly given its focus on mechanisms by which plants can adapt to changing environments. However, progress in this field is hindered by the inherent complexity of PI, which is featured by considerable species diversity and ecological variability. These factors give rise to a complex system of PI that remains poorly understood, encompassing plant behavior, substrate interactions, functional adaptations, and their interrelationships. Among these challenges, a fundamental bottleneck is how to evaluate PI in practical terms. A representative example is the ongoing debate over whether tree intelligence should be considered a higher or lower form of PI – a controversy stemming from the lack of unified criteria for quantification. To address this problem, we propose a heuristic concept of plant Intelligence Quotient (pIQ), aimed at evaluating the computational or adaptive capabilities of plants to enable quantitative assessment of PI. Further, we argue that integrating causal mechanisms of PI could lead to breakthroughs within the existing ecological paradigm. Research on PI also holds theoretical potential for advancing a wide range of disciplines, from biological science to planetary sustainability. Ultimately, this line of inquiry may support the development of novel, AI-parallel frameworks that extend from bionic to machine intelligence and, even, AI forefronts, thereby catalyzing scientific and engineering transformations across fields. -
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Lin Y. (2026). Advancing Plant Intelligence (PI) in parallel with AI: Proposal of plant Intelligence Quotient (pIQ) as a germinating point. The Innovation Informatics 2:100044. https://doi.org/10.59717/j.xinn-inform.2026.100044
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Lin Y. (2026). Advancing Plant Intelligence (PI) in parallel with AI: Proposal of plant Intelligence Quotient (pIQ) as a germinating point. The Innovation Informatics 2:100044. https://doi.org/10.59717/j.xinn-inform.2026.100044
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Figure 1.
The rise of PI as an AI-parallel branch. The schematic diagram of briefly reviewing
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Figure 2.
Publication trends of PI and AI (1926–2026) for comparison
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Figure 3.
Key behaviors and functions in PI research
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Figure 4.
The complexity of PI mechanism as a “knob lock” to unlock
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Figure 5.
The challenge of characterizing PI: Tree-dominated vs. herb-dominated species and ecosystems
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Figure 6.
Deriving pIQ from plant functional traits and the do-math capacity
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Figure 7.
Rethinking causal ecological analysis: PI as a missing x-factor
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Figure 8.
Interdisciplinary significance of exploring PI
