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Biomimetics
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Biomimetics in Intelligent Sensor: 2nd Edition
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Biomimetics in Intelligent Sensor: 2nd Edition

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: closed (20 December 2025) | Viewed by 2740

Special Issue Editors

Dr. Qian Wang

E-Mail Website
Guest Editor
Jiangsu Provincial Key Laboratory of Advanced Robotics, Soochow University, Suzhou 215021, China
Interests: bionic intelligent sensing; bio-inspired sensors; mechanical bionic
Special Issues, Collections and Topics in MDPI journals
Dr. Kejun Wang

E-Mail Website
Guest Editor
Jiangsu Provincial Key Laboratory of Advanced Robotics, Soochow University, Suzhou 215021, China
Interests: bionic intelligent sensing; bio-inspired mechano-sensors

Special Issue Information

Dear Colleagues,

With the rapid advancement of technology, the integration of biomimetics and intelligent sensors is opening up rich possibilities for innovative research and technological applications. This Special Issue aims to delve into the application of biomimetics in the field of intelligent sensors, providing an exchange platform for researchers in academia and industry to collaboratively drive progress in this domain.

Bio-sensing technology, as a crucial component of biomimetics, offers new perspectives for the design and enhancement of intelligent sensors via simulation and the application of sensing mechanisms found in biological systems. One point of emphasis for this Special Issue is biomimetic sensor design, involving the creation of various sensor structures and principles inspired by nature to enhance adaptability and sensitivity to environmental changes.

In the realm of smart materials application, this SI seeks inspiration from natural materials to achieve more efficient and flexible sensor performance. The introduction of bio-inspired algorithms provides a means of optimizing the design of intelligent sensors by simulating mechanisms such as evolution and genetics from biological systems, enabling adaptive performance optimization.

Biosignal processing, involving the conversion of biological signals into information with use for sensor systems, stands out as a key technology in this field. Simultaneously, research on sensor networks and biosensing is a focal point that aims to enable collaborative intelligent sensor research across various domains and broader applications.

This Special Issue will not only emphasize fundamental theoretical research but also intends to promote the practical application of intelligent sensors. In fields such as agriculture, healthcare, and environmental monitoring, the introduction of intelligent sensors offers novel approaches to the monitoring, analysis, and investigation of problems. By exploring the application of intelligent sensors in different domains, this Special Issue aims to provide valuable insights for researchers in related fields.

This Special Issue invites researchers and thinkers to actively participate in this field by submitting original research papers and sharing the latest achievements and breakthroughs related to biomimetics in intelligent sensors. We hope to stimulate greater reflection and innovation in the intersection of biomimetics and intelligent sensor research, driving technological advancements and contributing to solving societal and environmental issues.

Dr. Qian Wang
Dr. Kejun Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent sensors
  • bio-sensing technology
  • biomimetic sensor design
  • smart materials, bio-inspired algorithms
  • biosignal processing
  • sensor networks
  • biosensing
  • applications of intelligent sensors
  • bio-sensing manufacturing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 7557 KB  
Article
Machine Learning Distinguishes Plant Bioelectric Recordings with and Without Nearby Human Movement
by Peter A. Gloor and Moritz Weinbeer
Biomimetics 2025, 10(11), 776; https://doi.org/10.3390/biomimetics10110776 - 15 Nov 2025
Viewed by 1026
Abstract
Background: Quantitatively detecting whether plants exhibit measurable bioelectric differences in the presence of nearby human movement remains challenging, in part because plant signals are low-amplitude, slow, and easily confounded by environmental factors. Methods: We recorded bioelectric activity from 2978 plant samples [...] Read more.
Background: Quantitatively detecting whether plants exhibit measurable bioelectric differences in the presence of nearby human movement remains challenging, in part because plant signals are low-amplitude, slow, and easily confounded by environmental factors. Methods: We recorded bioelectric activity from 2978 plant samples across three species (basil, salad, tomato) using differential electrode pairs (leaf and soil electrodes) sampling at 142 Hz. Two trained performers executed three specific eurythmic gestures near experimental plants while control plants remained isolated. Random Forest and Convolutional Neural Network classifiers were applied to distinguish the control from treatment conditions using engineered features including spectral, temporal, wavelet, and frequency domain characteristics. Results: Random Forest classification achieved 62.7% accuracy (AUC = 0.67) distinguishing differences in recordings collected near a moving human from control conditions, representing a statistically significant 12.7 percentage point improvement over chance. Individual performer signatures were detectable with 68.2% accuracy, while plant species classification achieved only 44.5% accuracy, indicating minimal species-specific artifacts. Temporal analysis revealed that the plants with repeated exposure exhibited consistently less negative bioelectric amplitudes compared to single-exposure plants. Innovation: We introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions. Conclusions: Plants exhibit modest but statistically detectable bioelectric differences in the presence of nearby human movement. Rather than attributing these differences to eurythmic movement itself, the present design can only demonstrate that plant recordings collected within ~1 m of a moving human differ, modestly but statistically, from recordings taken ≥3 m away. The underlying biophysical pathways and specific contributing factors (airflow, VOCs, thermal plumes, vibration, electromagnetic fields) remain unknown. These results should therefore be interpreted as exploratory correlations, not mechanistic evidence of gesture-specific plant sensing. Full article
(This article belongs to the Special Issue Biomimetics in Intelligent Sensor: 2nd Edition)
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19 pages, 3295 KB  
Article
Structure Design and Performance Study of Bionic Electronic Nasal Cavity
by Pu Chen, Zhipeng Yin, Shun Xu, Pengyu Wang, Lianjun Yang and You Lv
Biomimetics 2025, 10(8), 555; https://doi.org/10.3390/biomimetics10080555 - 21 Aug 2025
Cited by 3 | Viewed by 1250
Abstract
A miniaturised bionic electronic nose system was developed to solve the problems of expensive equipment and long response time for soil pesticide residue detection. The structure of the bionic electronic nasal cavity is designed based on the spatial structure and olfactory principle of [...] Read more.
A miniaturised bionic electronic nose system was developed to solve the problems of expensive equipment and long response time for soil pesticide residue detection. The structure of the bionic electronic nasal cavity is designed based on the spatial structure and olfactory principle of the sturgeon nasal cavity. Through experimental study, the structure of the nasal cavity of the sturgeon was extracted and analyzed. The 3D model of the bionic electronic nasal cavity was constructed and verified by Computational Fluid Dynamics (CFD) simulation. The results show that the gas flow distribution in the bionic chamber is more uniform than that in the ordinary chamber. The airflow velocity near the sensor in the bionic chamber is lower than in the ordinary chamber. The eddy current intensity near the bionic chamber sensor is 2.29 times that of the ordinary chamber, further increasing the contact intensity between odor molecules and the sensor surface and shortening the response time. The 10-fold cross-validation method of K-Nearest Neighbor (K-NN), Random Forest (RF) and Support Vector Machine (SVM) was used to compare the recognition performance of the bionic electronic nasal cavity with that of the ordinary electronic nasal cavity. The results showed that, when the bionic electronic nose detection system identified the concentration of pesticide residues in soil, the recognition rate of the above three recognition algorithms reached 97.3%, significantly higher than that of the comparison chamber. The bionic chamber electronic nose system can improve the detection performance of electronic noses and has a good application prospect in soil pesticide residue detection. Full article
(This article belongs to the Special Issue Biomimetics in Intelligent Sensor: 2nd Edition)
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