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Selected Papers from the 11th International Electronic Conference on Sensors...
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Selected Papers from the 11th International Electronic Conference on Sensors and Applications

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

Deadline for manuscript submissions: closed (30 October 2025) | Viewed by 1629

Special Issue Editors

Prof. Dr. Stefano Mariani

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
Interests: MEMS; smart materials; micromechanics; machine learning-driven materials modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francisco Falcone

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Communication Engineering & Institute for Smart Cities (ISC), Public University of Navarre, 31006 Pamplona, Spain
Interests: wireless networks; performance evaluation; distributed systems; context-aware environments; IoT; next-generation wireless systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stefan Bosse

E-Mail Website
Guest Editor
Department Computer Science, University of Koblenz, Koblenz, Germany
Interests: artificial intelligence; machine learning; distributed systems; parallel systems; programming languages, self-organizing systems; multi-agent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jean-Marc Laheurte

E-Mail Website
Guest Editor
Laboratory of Electronics, SYstèmes de COmmunications and Microsystems, Université Gustave Eiffel, Champs-sur-Marne, France
Interests: antennas in matter; RFID technologies; RFID localization; body area networks (BANs) antennas and channel modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will comprise extended and expanded versions of proceedings papers from the 11th International Electronic Conference on Sensors and Applications (https://sciforum.net/event/ecsa-11), which is to be held 26–28 November 2024, on sciforum.net. In this 11th edition of the e-conference, contributors are invited to provide papers and presentations relataed to the field of sensors and their application. The papers that attract the most interest online or that provide a particularly innovative contribution will be published. These papers will be subjected to peer review and published in order to achieve the rapid and wide dissemination of research results, developments, and applications. We hope that this conference series will grow further in the future and become recognized as a means of (electronically) presenting recent developments in the field of sensors.

Prof. Dr. Stefano Mariani
Prof. Dr. Francisco Falcone
Dr. Stefan Bosse
Prof. Dr. Jean-Marc Laheurte
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

  • chemo- and biosensors
  • physical sensors
  • sensor networks, IoT, smart cities, and heath monitoring
  • sensors and artificial intelligence
  • smart agriculture sensors
  • electronic sensors, devices, and systems
  • wearable sensors and healthcare applications
  • robotics, sensors, and industry 4.0

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.

Published Papers (1 paper)

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Research

17 pages, 2986 KB  
Article
Physics-Aware Ensemble Learning for Superior Crop Recommendation in Smart Agriculture
by Hemalatha Gunasekaran, Krishnamoorthi Ramalakshmi, Saswati Debnath and Deepa Kanmani Swaminathan
Sensors 2025, 25(19), 6243; https://doi.org/10.3390/s25196243 - 9 Oct 2025
Viewed by 477
Abstract
Agriculture remains the backbone of many countries; it plays a pivotal role in shaping a country’s overall economy. Accurate prediction in agriculture practices, particularly crop recommendations, can greatly enhance productivity and resource management. IoT and AI technologies have great potential for enhancing precision [...] Read more.
Agriculture remains the backbone of many countries; it plays a pivotal role in shaping a country’s overall economy. Accurate prediction in agriculture practices, particularly crop recommendations, can greatly enhance productivity and resource management. IoT and AI technologies have great potential for enhancing precision farming; traditional machine learning (ML) and ensemble learning (EL) models rely primarily on the training data for predictions. When the training data is noisy or limited, these models can result in inaccurate or unrealistic predictions. These limitations are addressed by incorporating physical laws into the ML framework, thereby ensuring that the predictions remain physically plausible. In this study, we conducted a detailed analysis of ML and EL models, both with and without optimization, and compared their performance against a physics-informed ML model. In the proposed stacking physics-informed ML model, the optimal temperature and the pH for each crop (physics law) are provided as input during the training process in addition to the training data. The physics-informed model was trained to simultaneously satisfy two objectives: (1) fitting the data, and (2) adhering to the physics law. This was achieved by including a penalty term within its total loss function, forcing the model to make predictions that are both accurate and physically feasible. Our findings indicate that the proposed novel stacking physics-informed model achieved a highest accuracy of 99.50% when compared to ML and EL models with optimization. Full article
(This article belongs to the Special Issue Selected Papers from the 11th International Electronic Conference on Sensors and Applications)
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