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Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces
Review Article  ·  Published: 30 June 2026
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Journal of Plant Electrobiology
Volume 1, Issue 2, 2026: 123-140
Review Article Free to Read

Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces

by
Ziyang Wang Ziyang Wang 1 * ORCID
Fangmei Yang Fangmei Yang 1 ORCID
Ruihang Zhang Ruihang Zhang 2
Dongjie Zhao Dongjie Zhao 3 ORCID
1 Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2 School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China
3 School of Automation, Qingdao University, Qingdao 266071, China
* Corresponding Author: Ziyang Wang, [email protected]
Volume 1, Issue 2
Volume 1, Issue 2 2026
Received: 03 March 2026 Accepted: 24 June 2026 Published: 30 June 2026 CrossMark
Download PDF 1.82 MB Cite Metrics
DOI: 10.62762/JPE.2026.744863
ARK: ark:/57805/jpe.2026.744863

Article Information

Published in Journal of Plant Electrobiology
Volume/Issue Volume 1, Issue 2, 2026
Pages 123-140

Abstract

Plant electrophysiology is undergoing a profound paradigm shift from traditional phenomenological observation to systemic signal decoding, with mature methodologies from computational neuroscience and brain-computer interface technologies providing critical theoretical and engineering support for this interdisciplinary evolution. This review first systematically summarizes the evolution of flexible wearable electrodes and ultra-high impedance amplification hardware systems tailored to the ultra-slow signal dynamics and continuous morphological growth characteristics of plants. Second, we discuss the application pathways of introducing standardized sequential evoked paradigms from neuroscience—such as steady-state visual evoked potentials and event-related potentials—into the plant domain. This aims to replace traditional destructive stimuli with non-invasive, reproducible rhythmic stimulation to acquire data with high signal-to-noise ratios. In the dimension of data analysis, we explore modeling strategies that incorporate physics-informed neural networks and multi-modal heterogeneous sensor fusion technologies under the constraint of sample scarcity, aiming to resolve the equifinality problem inherent in single-modality electrical signal decoding. Building upon this decoding foundation, this paper proposes the construction of a bidirectional Plant-Computer Interface architecture, exploring the engineering feasibility of utilizing the plant itself as an active sensory node to directly drive closed-loop regulation within agricultural environments. Establishing cross-species standardized open-source datasets and unified hardware/software testing benchmarks will be the core driving force in overcoming current data fragmentation. Ultimately, the deep integration of multidisciplinary approaches will lay a rigorous scientific foundation for precision agricultural resource management and the development of next-generation bio-inspired intelligent hardware.

Graphical Abstract

Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces

Keywords

plant electrophysiology plant-computer interface multi-modal fusion signal decoding plant-in-the-loop soft robotics

Data Availability Statement

Not applicable.

Funding

This work was supported by the Development and Promotion of a Flexible, Low-Damage Qingdao Key Technology R&D Project: Multi-Row Intelligent Fresh Corn Combine Harvester.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that Gemini 3.1 was used for language editing and translation of the manuscript. The authors have carefully reviewed, revised, and verified the AI-assisted output and take full responsibility for the content of the manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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APA Style
Wang, Z., Yang, F., Zhang, R., & Zhao, D. (2026). Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces. Journal of Plant Electrobiology, 1(2), 123-140. https://doi.org/10.62762/JPE.2026.744863
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TY  - JOUR
AU  - Wang, Ziyang
AU  - Yang, Fangmei
AU  - Zhang, Ruihang
AU  - Zhao, Dongjie
PY  - 2026
DA  - 2026/06/30
TI  - Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces
JO  - Journal of Plant Electrobiology
T2  - Journal of Plant Electrobiology
JF  - Journal of Plant Electrobiology
VL  - 1
IS  - 2
SP  - 123
EP  - 140
DO  - 10.62762/JPE.2026.744863
UR  - https://www.icck.org/article/abs/JPE.2026.744863
KW  - plant electrophysiology
KW  - plant-computer interface
KW  - multi-modal fusion
KW  - signal decoding
KW  - plant-in-the-loop
KW  - soft robotics
AB  - Plant electrophysiology is undergoing a profound paradigm shift from traditional phenomenological observation to systemic signal decoding, with mature methodologies from computational neuroscience and brain-computer interface technologies providing critical theoretical and engineering support for this interdisciplinary evolution. This review first systematically summarizes the evolution of flexible wearable electrodes and ultra-high impedance amplification hardware systems tailored to the ultra-slow signal dynamics and continuous morphological growth characteristics of plants. Second, we discuss the application pathways of introducing standardized sequential evoked paradigms from neuroscience—such as steady-state visual evoked potentials and event-related potentials—into the plant domain. This aims to replace traditional destructive stimuli with non-invasive, reproducible rhythmic stimulation to acquire data with high signal-to-noise ratios. In the dimension of data analysis, we explore modeling strategies that incorporate physics-informed neural networks and multi-modal heterogeneous sensor fusion technologies under the constraint of sample scarcity, aiming to resolve the equifinality problem inherent in single-modality electrical signal decoding. Building upon this decoding foundation, this paper proposes the construction of a bidirectional Plant-Computer Interface architecture, exploring the engineering feasibility of utilizing the plant itself as an active sensory node to directly drive closed-loop regulation within agricultural environments. Establishing cross-species standardized open-source datasets and unified hardware/software testing benchmarks will be the core driving force in overcoming current data fragmentation. Ultimately, the deep integration of multidisciplinary approaches will lay a rigorous scientific foundation for precision agricultural resource management and the development of next-generation bio-inspired intelligent hardware.
SN  - 3071-6268
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Wang2026Neuroscien,
  author = {Ziyang Wang and Fangmei Yang and Ruihang Zhang and Dongjie Zhao},
  title = {Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces},
  journal = {Journal of Plant Electrobiology},
  year = {2026},
  volume = {1},
  number = {2},
  pages = {123-140},
  doi = {10.62762/JPE.2026.744863},
  url = {https://www.icck.org/article/abs/JPE.2026.744863},
  abstract = {Plant electrophysiology is undergoing a profound paradigm shift from traditional phenomenological observation to systemic signal decoding, with mature methodologies from computational neuroscience and brain-computer interface technologies providing critical theoretical and engineering support for this interdisciplinary evolution. This review first systematically summarizes the evolution of flexible wearable electrodes and ultra-high impedance amplification hardware systems tailored to the ultra-slow signal dynamics and continuous morphological growth characteristics of plants. Second, we discuss the application pathways of introducing standardized sequential evoked paradigms from neuroscience—such as steady-state visual evoked potentials and event-related potentials—into the plant domain. This aims to replace traditional destructive stimuli with non-invasive, reproducible rhythmic stimulation to acquire data with high signal-to-noise ratios. In the dimension of data analysis, we explore modeling strategies that incorporate physics-informed neural networks and multi-modal heterogeneous sensor fusion technologies under the constraint of sample scarcity, aiming to resolve the equifinality problem inherent in single-modality electrical signal decoding. Building upon this decoding foundation, this paper proposes the construction of a bidirectional Plant-Computer Interface architecture, exploring the engineering feasibility of utilizing the plant itself as an active sensory node to directly drive closed-loop regulation within agricultural environments. Establishing cross-species standardized open-source datasets and unified hardware/software testing benchmarks will be the core driving force in overcoming current data fragmentation. Ultimately, the deep integration of multidisciplinary approaches will lay a rigorous scientific foundation for precision agricultural resource management and the development of next-generation bio-inspired intelligent hardware.},
  keywords = {plant electrophysiology, plant-computer interface, multi-modal fusion, signal decoding, plant-in-the-loop, soft robotics},
  issn = {3071-6268},
  publisher = {Institute of Central Computation and Knowledge}
}

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Journal of Plant Electrobiology
Journal of Plant Electrobiology
ISSN: 3071-6268 (Online)
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