sensors-logo

Journal Browser

Journal Browser

Sensors in 2025

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 5035

Special Issue Editors

Department of Innovation Engineering, University of Salento, Via Monteroni, 73100 Lecce, Italy
Interests: Internet of Things; computer networks; cloud networks; RFID and BLE technologies; localization; smart environments; AAL systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Information Technology and Electrical Engineering, University of Napoli Federico II, Naples, Italy
Interests: measurement in the IoT field and, more generally, in the Industry 4.0 and Health 4.0 fields; cyber-physical measurement systems; measurement of ICT systems sustainability and sustainability of measurements; operation and performance assessment of communication systems, equipment, and networks; measurement uncertainty; impact of quantum technologies on measurements; metrological characterization of advanced human-to-machine interfaces
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettrica e dell'Informazione (Department of Electrical and Information Engineering), Politecnico di Bari, Via Edoardo Orabona n. 4, 70125 Bari, Italy
Interests: optoelectronic technologies; photonic devices and sensors; nanophotonic integrated sensors; non linear integrated optics; microelectronic and nanoelectronic technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue, entitled “Sensors in 2025”, which is part of the MDPI journal New Year Special Issue Series. This Special Issue will be a collection of high-quality reviews and original research articles from Advisory Board Members, Editors-in-Chief, Editorial Board Members, Guest Editors, Topical Advisory Panel Members, Reviewer Board Members, Societies, Authors, and Reviewers from Sensors, in addition to excellent editorials from high-profile scholars in the sensors field. Submissions on all aspects of sensors and sensing technologies are welcome.

We welcome submissions from all authors, irrespective of gender.

Dr. Luigi Patrono
Prof. Dr. Leopoldo Angrisani
Prof. Dr. Vittorio M. N. Passaro
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • physical sensors
  • chemical sensors
  • biosensors
  • biomedical sensors
  • lab-on-a-chip
  • remote sensors
  • sensor networks

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (9 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

18 pages, 7869 KiB  
Article
AdapTree: Data-Driven Approach to Assessing Plant Stress Through the AI-Sensor Synergy
by Divisha Garg, Harpreet Singh and Yosi Shacham-Diamand
Sensors 2025, 25(10), 3149; https://doi.org/10.3390/s25103149 - 16 May 2025
Viewed by 189
Abstract
This study investigates plant stress assessment by integrating advanced sensor technologies and Artificial Intelligence (AI). Multi-sensor data—including electrical impedance spectroscopy, temperature, and humidity—were used to capture plant physiological responses under environmental stress conditions. The key task addressed was the prediction of stress-related parameters [...] Read more.
This study investigates plant stress assessment by integrating advanced sensor technologies and Artificial Intelligence (AI). Multi-sensor data—including electrical impedance spectroscopy, temperature, and humidity—were used to capture plant physiological responses under environmental stress conditions. The key task addressed was the prediction of stress-related parameters using machine learning. A novel boosting-based ensemble method, AdapTree, combining AdaBoost and decision trees, was proposed to improve predictive accuracy and model interpretability. Experimental evaluation across multiple regression metrics demonstrated that AdapTree outperformed baseline models, achieving an R2 score of 0.993 for impedance magnitude prediction and 0.999 for both relative humidity (RH) and temperature, along with low root mean squared error (134.565 for impedance, 0.006966 for RH, and 0.0050099 for temperature) and mean absolute error values (22.789 for impedance; 1.51 × 105 for RH and 2.51 × 105 for temperature). These findings validate the reliability and effectiveness of the proposed AI-driven framework in accurately interpreting sensor data for plant stress detection. The approach offers a scalable, data-driven solution to enhance precision agriculture and agricultural sustainability. Furthermore, this method can be extended to monitor additional stress markers or applied across diverse plant species and field conditions, supporting future developments in intelligent crop monitoring systems. Full article
(This article belongs to the Special Issue Sensors in 2025)
Show Figures

Figure 1

15 pages, 7160 KiB  
Article
Dual-Band Dual-Beam Shared-Aperture Reflector Antenna Design with FSS Subreflector
by Qunbiao Wang, Peng Li, Guodong Tan, Yiqun Zhang, Yuanxin Yan, Wanye Xu and Paolo Rocca
Sensors 2025, 25(9), 2934; https://doi.org/10.3390/s25092934 - 6 May 2025
Viewed by 173
Abstract
In this study, a dual-band dual-beam shared-aperture reflector antenna based on a Cassegrain configuration is designed using a frequency-selective surface (FSS) subreflector. The antenna generates two shaped beams that operate at different frequencies and can spatially overlap. One beam contour can be independently [...] Read more.
In this study, a dual-band dual-beam shared-aperture reflector antenna based on a Cassegrain configuration is designed using a frequency-selective surface (FSS) subreflector. The antenna generates two shaped beams that operate at different frequencies and can spatially overlap. One beam contour can be independently optimized by properly designing the shape of the main reflector. The contour of the second beam is defined by optimizing the unit cell and geometry of the FSS-based subreflector once the shape of the main reflector is set. The reflector antenna design is cast as the optimization of a suitably defined cost function aimed at yielding the desired directivity performance in the regions of coverage. In order to validate the proposed solution, a set of numerical experiments was conducted using most of China and Shaanxi province as benchmark examples. Full article
(This article belongs to the Special Issue Sensors in 2025)
Show Figures

Figure 1

18 pages, 4885 KiB  
Article
Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring
by Venkatraman Manikandan and Suresh Neethirajan
Sensors 2025, 25(9), 2912; https://doi.org/10.3390/s25092912 - 5 May 2025
Viewed by 451
Abstract
Acoustic monitoring presents a promising, non-invasive modality for assessing animal welfare in precision livestock farming. In poultry, vocalizations encode biologically relevant cues linked to health status, behavioral states, and environmental stress. This study proposes an integrated analytical framework that combines signal-level statistical analysis [...] Read more.
Acoustic monitoring presents a promising, non-invasive modality for assessing animal welfare in precision livestock farming. In poultry, vocalizations encode biologically relevant cues linked to health status, behavioral states, and environmental stress. This study proposes an integrated analytical framework that combines signal-level statistical analysis with machine learning and deep learning classifiers to interpret chicken vocalizations in a welfare assessment context. The framework was evaluated using three complementary datasets encompassing health-related vocalizations, behavioral call types, and stress-induced acoustic responses. The pipeline employs a multistage process comprising high-fidelity signal acquisition, feature extraction (e.g., mel-frequency cepstral coefficients, spectral contrast, zero-crossing rate), and classification using models including Random Forest, HistGradientBoosting, CatBoost, TabNet, and LSTM. Feature importance analysis and statistical tests (e.g., t-tests, correlation metrics) confirmed that specific MFCC bands and spectral descriptors were significantly associated with welfare indicators. LSTM-based temporal modeling revealed distinct acoustic trajectories under visual and auditory stress, supporting the presence of habituation and stressor-specific vocal adaptations over time. Model performance, validated through stratified cross-validation and multiple statistical metrics (e.g., F1-score, Matthews correlation coefficient), demonstrated high classification accuracy and generalizability. Importantly, the approach emphasizes model interpretability, facilitating alignment with known physiological and behavioral processes in poultry. The findings underscore the potential of acoustic sensing and interpretable AI as scalable, biologically grounded tools for real-time poultry welfare monitoring, contributing to the advancement of sustainable and ethical livestock production systems. Full article
(This article belongs to the Special Issue Sensors in 2025)
Show Figures

Figure 1

14 pages, 4647 KiB  
Article
Rotary Panoramic and Full-Depth-of-Field Imaging System for Pipeline Inspection
by Qiang Xing, Xueqin Zhao, Kun Song, Jiawen Jiang, Xinhao Wang, Yuanyuan Huang and Haodong Wei
Sensors 2025, 25(9), 2860; https://doi.org/10.3390/s25092860 - 30 Apr 2025
Viewed by 216
Abstract
To address the adaptability and insufficient imaging quality of conventional in-pipe imaging techniques for irregular pipelines or unstructured scenes, this study proposes a novel radial rotating full-depth-of-field focusing imaging system designed to adapt to the structural complexities of irregular pipelines, which can effectively [...] Read more.
To address the adaptability and insufficient imaging quality of conventional in-pipe imaging techniques for irregular pipelines or unstructured scenes, this study proposes a novel radial rotating full-depth-of-field focusing imaging system designed to adapt to the structural complexities of irregular pipelines, which can effectively acquire tiny details with a depth of 300–960 mm inside the pipeline. Firstly, a fast full-depth-of-field imaging method driven by depth features is proposed. Secondly, a full-depth rotating imaging apparatus is developed, incorporating a zoom camera, a miniature servo rotation mechanism, and a control system, enabling 360° multi-view angles and full-depth-of-field focusing imaging. Finally, full-depth-of-field focusing imaging experiments are carried out for pipelines with depth-varying characteristics. The results demonstrate that the imaging device can acquire depth data of the pipeline interior and rapidly obtain high-definition characterization sequence images of the inner pipeline wall. In the depth-of-field segmentation with multiple view angles, the clarity of the fused image is improved by 75.3% relative to a single frame, and the SNR and PSNR reach 6.9 dB and 26.3 dB, respectively. Compared to existing pipeline closed-circuit television (CCTV) and other in-pipeline imaging techniques, the developed rotating imaging system exhibits high integration, faster imaging capabilities, and adaptive capacity. This system provides an adaptive imaging solution for detecting defects on the inner surfaces of irregular pipelines, offering significant potential for practical applications in pipeline inspection and maintenance. Full article
(This article belongs to the Special Issue Sensors in 2025)
Show Figures

Figure 1

11 pages, 1016 KiB  
Article
Validity of the Quarq Cycling Power Meter
by Jon Oteo-Gorostidi, Jesús Camara, Diego Ojanguren-Rodríguez, Jon Iriberri, Iván Vadillo-Ventura and Almudena Montalvo-Pérez
Sensors 2025, 25(9), 2717; https://doi.org/10.3390/s25092717 - 25 Apr 2025
Viewed by 454
Abstract
Technological advancements have led to the development of various devices designed to monitor training loads and athletic performance. Power meters, particularly in cycling, allow for the precise quantification of power output, which is crucial for managing training loads and evaluating performance improvements. This [...] Read more.
Technological advancements have led to the development of various devices designed to monitor training loads and athletic performance. Power meters, particularly in cycling, allow for the precise quantification of power output, which is crucial for managing training loads and evaluating performance improvements. This study evaluates the validity of the Quarq D-Zero power meter for measuring cycling power output by comparing it with two previously validated devices—the Favero Assioma Duo (FAD) and the Hammer Saris H3 (H3)—noting that, although it shares the same measurement location as the SRM (the gold standard), it has not been directly validated against it. Thirty-one trained male cyclists participated in this study, undergoing tests across various power outputs (100–500 W) and three 10-s sprint efforts. The protocol incorporated different cadences (70, 85, and 100 revolutions per minute), randomized in order, and two cycling positions (seated and standing). Significant differences (p < 0.05) in power readings were observed among the three power meters, except during sprint efforts. However, pairwise comparisons revealed no significant differences (p > 0.05) between the FAD and Quarq power meters, except for the 500 W block. Strong to very strong correlations were observed between the FAD and Quarq power meters (r > 0.883, ICC > 0.879). The coefficient of variation (CV) between the FAD and Quarq devices ranged from 0.62% to 4.89%, and from 0.39% to 6.59% between the H3 and Quarq power meters. In conclusion, the Quarq power meter, integrated into the spider of the bicycle’s bottom bracket, provides valid power output measurements in cycling. Full article
(This article belongs to the Special Issue Sensors in 2025)
Show Figures

Figure 1

23 pages, 16173 KiB  
Article
Enhanced Prediction of Soil Carbon via Encoder-Decoder Neural Networks for a Boreal Study Area in Northern Ontario
by Rory Pittman and Baoxin Hu
Sensors 2025, 25(8), 2583; https://doi.org/10.3390/s25082583 - 19 Apr 2025
Viewed by 195
Abstract
Addressing the impacts of carbon in connection with land cover conversion and climate change is of predominant interest for boreal realms. Consequently, boosting accuracy for the prediction of total carbon (C) with soil mapping is a crucial objective, particularly for a boreal study [...] Read more.
Addressing the impacts of carbon in connection with land cover conversion and climate change is of predominant interest for boreal realms. Consequently, boosting accuracy for the prediction of total carbon (C) with soil mapping is a crucial objective, particularly for a boreal study area under risk of land cover transition in northern Ontario, Canada. To enhance the prediction of soil modeling, integrated approaches combining encoder-decoder (ED) with dense neural network (DNN) and convolutional neural network (CNN) formulations suitable for smaller target data sets were developed. These methods were able to effectively extract dominant features within predictor data and augment modeling accuracy. The obtained results were compared with those attained from structural equation modeling (SEM) and random forest (RF), as well as basic DNN and CNN models. A model ensemble based on all approaches was also considered, from which standard deviations were calculated to gauge the prediction uncertainty. Quantile mappings with respect to deciles were also derived from the model ensemble to provide additional insights with prediction. Validation accuracies for the ED-CNN model attained a coefficient of determination (R2) of 0.59. The greatest deviations with predicting C contents corresponded to the wetlands. However, when quantified by decile mapping, forested localities within river valleys encountered the highest uncertainties with prediction, indicting a need for better modeling of sites with intermediate concentrations of soil C. Full article
(This article belongs to the Special Issue Sensors in 2025)
Show Figures

Figure 1

14 pages, 5317 KiB  
Article
LITES-Based Sensitive CO2 Detection Using 2 μm Diode Laser and Self-Designed 9.5 kHz Quartz Tuning Fork
by Junjie Mu, Jinfeng Hou, Shaoqi Qiu, Shunda Qiao, Ying He and Yufei Ma
Sensors 2025, 25(7), 2099; https://doi.org/10.3390/s25072099 - 27 Mar 2025
Viewed by 254
Abstract
A carbon dioxide (CO2) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2 μm diode laser and a self-designed low-frequency trapezoidal-head QTF is reported for the first time in this invited paper. The self-designed trapezoidal-head QTF with a low resonant [...] Read more.
A carbon dioxide (CO2) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2 μm diode laser and a self-designed low-frequency trapezoidal-head QTF is reported for the first time in this invited paper. The self-designed trapezoidal-head QTF with a low resonant frequency of 9464.18 Hz and a high quality factor (Q) of 12,133.56 can significantly increase the accumulation time and signal level of the CO2-LITES sensor. A continuous-wave (CW) distributed-feedback (DFB) diode laser is used as the light source, and the strongest absorption line of CO2 located at 2004.01 nm is chosen. A comparison between the standard commercial QTF with the resonant frequency of 32.768 kHz and the self-designed trapezoidal-head QTF is performed. The experimental results show that the CO2-LITES sensor with the self-designed trapezoidal-head QTF has an excellent linear response to CO2 concentration, and its minimum detection limit (MDL) can reach 46.08 ppm (parts per million). When the average time is increased to 100 s based on the Allan variance analysis, the MDL of the sensor can be improved to 3.59 ppm. Compared with the 16.85 ppm of the CO2-LITES sensor with the commercial QTF, the performance is improved by 4.7 times, demonstrating the superiority of the self-designed trapezoidal-head QTF. Full article
(This article belongs to the Special Issue Sensors in 2025)
Show Figures

Figure 1

Review

Jump to: Research

30 pages, 7595 KiB  
Review
Principle and Applications of Thermoelectric Generators: A Review
by Mohamad Ridwan, Manel Gasulla and Ferran Reverter
Sensors 2025, 25(8), 2484; https://doi.org/10.3390/s25082484 - 15 Apr 2025
Viewed by 677
Abstract
For an extensive and sustainable deployment of technological ecosystems such as the Internet of Things, it is a must to leverage the free energy available in the environment to power the autonomous sensors. Among the different alternatives, thermal energy harvesters based on thermoelectric [...] Read more.
For an extensive and sustainable deployment of technological ecosystems such as the Internet of Things, it is a must to leverage the free energy available in the environment to power the autonomous sensors. Among the different alternatives, thermal energy harvesters based on thermoelectric generators (TEGs) are an attractive solution for those scenarios in which a gradient of temperature is present. In such a context, this article reviews the operating principle of TEGs and then the applications proposed in the literature in the last years. These applications are subclassified into five categories: domestic, industrial, natural heat, wearable, and others. In each category, a comprehensive comparison is carried out, including the thermal, mechanical, and electrical information of each case. Finally, an identification of the challenges and opportunities of research in the field of TEGs applied to low-power sensor nodes is exposed. Full article
(This article belongs to the Special Issue Sensors in 2025)
Show Figures

Figure 1

19 pages, 8454 KiB  
Review
A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives
by Xuan Li, Bingxue Zhu, Sijia Li, Lushi Liu, Kaishan Song and Jiping Liu
Sensors 2025, 25(8), 2345; https://doi.org/10.3390/s25082345 - 8 Apr 2025
Viewed by 506
Abstract
Chlorophyll absorbs light energy and converts it into chemical energy, making it a crucial biochemical parameter for monitoring vegetation health, detecting environmental stress, and predicting physiological states. Accurate and rapid estimation of canopy chlorophyll content is crucial for assessing vegetation dynamics, ecological changes, [...] Read more.
Chlorophyll absorbs light energy and converts it into chemical energy, making it a crucial biochemical parameter for monitoring vegetation health, detecting environmental stress, and predicting physiological states. Accurate and rapid estimation of canopy chlorophyll content is crucial for assessing vegetation dynamics, ecological changes, and growth patterns. Remote sensing technology has become an indispensable tool for monitoring vegetation chlorophyll content since 2015, with more than 50 research papers published annually, contributing to a substantial body of case studies. This review discusses remote sensing technologies currently used for estimating vegetation chlorophyll content, focusing on four key aspects: the acquisition of reference datasets, the identification of optimal spectral variables, the selection of estimation models, and the analysis of application scenarios. The results indicate that spectral bands in the visible and red-edge regions (e.g., 530 nm, 670 nm, and 705 nm) provide high prediction accuracy. Machine learning methods, such as random forest and support vector regression, exhibit excellent performance, with determination coefficients (R2) typically exceeding 0.9, although overfitting remains an issue. Although radiative transfer models are slightly less accurate (R2 = 0.6–0.8), they provide greater interpretability. Hybrid models integrating machine learning and radiative transfer show strong potential to balance accuracy and generalizability. Future research should improve model generalizability for different vegetation types and environmental conditions and integrate multi-source remote sensing data to improve spatial and temporal resolution. Combining physical models with data processing methods, such as artificial intelligence, can improve scalability, cost-effectiveness, and real-time monitoring capabilities. Full article
(This article belongs to the Special Issue Sensors in 2025)
Show Figures

Figure 1

Back to TopTop