Plant-Inspired Data Forms in Computation

Updated 19 October 2025

Plants-inspired data forms are rigorous algorithmic frameworks that emulate plant morphogenesis through mathematical and bioinspired models.
They combine category theory, Petri nets, deep generative models, and biohybrid sensors to capture both static structures and dynamic biochemical processes.
These approaches propel innovation in computational biology, unconventional computing, and adaptive communication networks for resilient data processing.

Plants-inspired data forms are rigorous, algorithmic, and structural frameworks that emulate principles derived from plant morphogenesis, computation, physiology, and communication to shape the organization, representation, and processing of data in scientific, computational, and engineering contexts. These forms range from mathematical abstractions of cellular architectures to hybrid biological–artificial systems and bioinspired communication channels, reflecting both the spatial structure and dynamic function characteristic of plant systems.

1. Formalisms for Structural and Biochemical Plant Data

The integration of category theory and Petri nets provides a robust dual-language for representing plant morphogenesis (Rudskiy, 2017). In this formalism:

Category Theory: Encodes the static, compositional architecture of chemical reactions and cell interactions (using traced monoidal categories). Objects (A, B) correspond to chemical substances or locations, morphisms (f: A → B) represent reaction pathways. Operations such as composition (f ∘ g) and tensor products (f ⊗ g) allow for modeling concurrent interactions.
Petri Net Approach: Models the dynamic execution of metabolic networks. Places represent chemical substrates, transitions correspond to reactions, and tokens signify the quantity or presence of substances. The movement of tokens according to enabling and firing rules simulates the temporal evolution of the biochemical network.

By employing these tools, both the genealogical (cell lineage, division history) and metabolic (substance flow, reaction events) relationships within the plant are formalized, allowing seamless interoperability between static (blueprint) and dynamic (process) perspectives through the construction of adjoint functors. The adjointness Hom₍S₎(L(c), s) ≈ Hom₍C₎(c, R(s)) mathematically guarantees reversible mappings between biochemical and structural representations.

This “dual data form” directly models both the spatial arrangement (graph G, tree T) and the functional metabolic state (Petri net diagram, traced category) of plant tissues, serving as a template for gene interaction networks.

2. Plant-Inspired Physical and Computational Processors

Plant roots and their growth dynamics underpin unconventional computing architectures (Adamatzky et al., 2017). In morphological plant processors:

Input Data: Encoded in the spatial configuration of attractants (nutrients) and repellents (toxins/obstacles) that steer root growth; roots find optimal paths, instantiate network topologies, and solve geometric problems (shortest path, MST, Voronoi diagrams).
Output Data: Realized in the final topology of the root network—an embodiment of the solution to a combinatorial or geometric computation formalized by cost minimization integrals or energy landscapes.

Table: Examples of Morphological Computation by Roots

Problem	Data Representation	Computation Embodiment
Shortest Path	Source, destination, gradient	Root path minimizing cost (∫ₚ d(x, y) ds)
Minimum Spanning Tree	Arrangement of nutrient points	Tree-like network of fused roots
Voronoi Diagram	Seed points	Radially growing roots, collision boundaries

On the electronics side, plants modified with nanoparticles or conductive polymers act as analog components (variable resistors, capacitors, memristors), enabling summing, integrating, differentiating, and logical operations. Circuit equations (e.g., v₀ = –(R₀/R₁)v₁ – (R₀/R₂)v₂) abstract the functionalization, and collision-based computation by root interaction supports logical processing.

3. Digital and Generative Plant Data Models

Advances in deep generative models foster new synthetic plant data forms for phenotyping (Giuffrida et al., 2017). ARIGAN, a conditional GAN, synthesizes realistic, labeled images of Arabidopsis rosettes:

Input Modalities: Noise vector z and condition vector y (one-hot leaf count).
Network Architecture: Two fully connected layers followed by five deconvolutional layers (stride 2, 5×5 filters), yielding 128×128 RGB images conditioned on leaf count.
Output Data: Synthetic plant images (Ax dataset) with explicit morphological labels.
Impact: Augments training for phenotyping algorithms, reduces overfitting, and enables controlled expansion of annotated datasets.

Conditioning (via one-hot encoding) enables directed morphogenesis in silico, and the GAN objective (min₍G₎ max₍D₎ V(G, D)) formalizes adversarial training for data realism.

4. Distributed and Adaptive Data Forms in Artificial Growth

Bioinspired controllers for adaptive material growth, such as the Vascular Morphogenesis Controller (VMC), instantiate distributed, plant-like decision architectures (Hofstadler et al., 2018):

Data Encoding in Structure: Each node records state variables—Resource R, Success S, Vessels V₁, V₂,…Vₙ—paralleling plant hormones and vascular allocation.
Decision Process: Local sensors compute S, propagate signals, update vessel thickness V (via parametrized rules ω, ρ, α, β), and distribute resources R to branches with highest S.
Physical Realization: Braided modules grow in response, integrating digital feedback with manual or potentially autonomous fabrication.

This distributed, feedback-driven model translates plant resource competition into adaptive, modular architectures applicable to material science and responsive design.

5. Phenotypic and Morphological Data Extraction

Extraction of highly granular morphological and physiological data leverages advanced computer vision and shape analysis:

Shape-Only Feature Sets: Local area integral invariants (LAIIs) and traditional geometric descriptors (solidity, circularity) encode multi-scale curvature and boundary energy in leaves (Hewitt et al., 2018). These features robustly classify species across lighting and scale variations.
Fine-Grained Detection for Robotics: CenterNet-based models with keypoint-guided polyline annotations delineate leaf, stem, and vein instances (Güldenring et al., 2023). Structurally flexible pseudo keypoints and true anatomical landmarks are regressed via polar coordinates (d, α), enabling detailed, hierarchical data encoding for agricultural robotics.

Evaluation metrics such as Projected OKS (POKS) adapt keypoint similarity to flexible anatomical trajectories, optimizing detection fidelity for elongated plant structures.

6. Physiological Signals and Biohybrid Data Flows

Electrophysiological measurements provide a data form for real-time plant state monitoring in uncontrolled environments (Buss et al., 30 Jun 2025):

Acquisition: Continuous potential recording with weatherproof, solar-powered PhytoNode sensors (sampled at 200 Hz, downsampled to 1 Hz).
Preprocessing: Z-score and min–max normalization; statistical feature extraction (over 700 features per hourly segment) using tsfresh.
Machine Learning Analysis: Binary state classifiers (Random Forest, MLP, SVM) and automated pipelines (AutoML) reach up to 95% macro F1-score for environmental condition discrimination.

Biohybrid integration (plants + wearable sensor networks) “flows” physiological data into machine reasoning pipelines, supporting scalable green monitoring.

7. Communication-Inspired Data Forms and Networks

Recent theoretical advances map plant intercellular and interplant communication into ICT frameworks, inspiring novel data forms and bioinspired networks (Kilic et al., 10 Sep 2025):

Transmitter–Channel–Receiver Paradigm: Plant chemical emission (e.g., VOC pulse modeled as m(t)), advection–diffusion–reaction channels (Green’s function c(r, t)), and receptor uptake (Robin condition: –D∂c/∂n = kₐ(c – c_int)).
Encoding Schemes: Modulation by concentration (CSK) or blend ratios (RSK) draws from natural stress signaling.
Distributed Network: The “Internet of Plants (IoP)” concept frames multispecies, multimodal communication networks for ecosystem monitoring, smart agriculture, and resilience analysis.

Table: Plant Communication Modalities in ICT Terms

Modality	ICT Analog	Mathematical Model
Chemical (VOC)	Molecular Comm.	Advection–Diffusion–Reaction Eq.
Electrical	Pulse Circuits	Cable Eq., Hodgkin–Huxley Models
Mycorrhizal	Graph Networks	Laplacian, Connectivity Analysis
Acoustic	Impulse Events	Frequency Spectrum, Signal Detection

These models inform robust, resource-efficient data transmission analogs patterned after plant signaling strategies.

Plants-inspired data forms encompass a spectrum of mathematical abstractions, computational architectures, and multimodal representations that directly reflect biological principles. They undergird research advances in computational biology, unconventional computing, synthetic data generation, adaptive architecture, biohybrid sensing, and communication system design, supporting the synthesis of resilient, context-aware, and information-rich scientific models rooted in the foundational processes of plant life.