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Sensors
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Flexible and Wearable Sensors and Sensing for Agriculture and Food
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Flexible and Wearable Sensors and Sensing for Agriculture and Food

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2812

Special Issue Editors

Prof. Dr. Xiaoshuan Zhang

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Guest Editor
Department of Mechatronics at the College of Engineering, Beijing Lab of Food Quality and Safety, China Agricultural University (East Campus), Beijing 100083, China
Interests: sensors (IoT, flexible sensors) and data processing in food supply chain/industrial engineering; live animal management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stevan Stankovski

E-Mail Website
Guest Editor
Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 106314 Novi Sad, Serbia
Interests: electronics and information technology; Internet of Things; control
Dr. Xinqing Xiao

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: smart sensing; smart agriculture; Internet of Things; energy harvesting sensing; self-powered sensing; battery-free sensing; food monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Marina A. Nikitina

E-Mail Website
Guest Editor
V.M. Gorbatov Federal Research Center for Foods Systems of RAS, 26, Talalikhina Str., 109316 Moscow, Russia
Interests: economic analysis and intelligent Information technology

Special Issue Information

Dear Colleagues,

Recently, with the rapid development of flexible technology, its applications are becoming more and more widespread. The better flexibility, ductility and biocompatibility of flexible sensors compared to rigid sensors have the potential to have a significant impact on the wearable and agrifood sectors. Therefore, this Special Issue aims to bring together original research and review articles on the latest advances, technologies, solutions, applications and new challenges of flexible sensing technologies, especially wearable flexible technologies, in the field of agriculture and food.

Potential topics include but are not limited to:

(1) Novel preparation method for flexible sensors;

(2) Low-cost flexible sensors;

(3) The application of flexible technology in the field of agriculture;

(4) Flexible technology in the field of non-destructive testing of food;

(5) Wearable sensing technology for the monitoring and detection of living organisms;

(6) Low-cost flexible diagnostic techniques for food analysis;

(7) New indicator technology for agrifood.

 

Prof. Dr. Xiaoshuan Zhang
Prof. Dr. Stevan Stankovski
Dr. Xinqing Xiao
Dr. Marina A. Nikitina
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.

Published Papers (2 papers)

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Research

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22 pages, 8461 KiB  
Article
Wearable Bioimpedance-Based Deep Learning Techniques for Live Fish Health Assessment under Waterless and Low-Temperature Conditions
by Yongjun Zhang, Longxi Chen, Huanhuan Feng, Xinqing Xiao, Marina A. Nikitina and Xiaoshuan Zhang
Sensors 2023, 23(19), 8210; https://doi.org/10.3390/s23198210 - 30 Sep 2023
Cited by 2 | Viewed by 1164
Abstract
(1) Background: At present, physiological stress detection technology is a critical means for precisely evaluating the comprehensive health status of live fish. However, the commonly used biochemical tests are invasive and time-consuming and cannot simultaneously monitor and dynamically evaluate multiple stress levels in [...] Read more.
(1) Background: At present, physiological stress detection technology is a critical means for precisely evaluating the comprehensive health status of live fish. However, the commonly used biochemical tests are invasive and time-consuming and cannot simultaneously monitor and dynamically evaluate multiple stress levels in fish and accurately classify their health levels. The purpose of this study is to deploy wearable bioelectrical impedance analysis (WBIA) sensors on fish skin to construct a deep learning-based stress dynamic evaluation model for precisely estimating their accurate health status. (2) Methods: The correlation of fish (turbot) muscle nutrients and their stress indicators are calculated using grey relation analysis (GRA) for allocating the weight of the stress factors. Next, WBIA features are sieved using the maximum information coefficient (MIC) in stress trend evaluation modeling, which is closely related to the key stress factors. Afterward, a convolutional neural network (CNN) is utilized to obtain the features of the WBIA signals. Then, the long short-term memory (LSTM) method learns the stress trends with residual rectification using bidirectional gated recurrent units (BiGRUs). Furthermore, the Z-shaped fuzzy function can accurately classify the fish health status by the total evaluated stress values. (3) Results: The proposed CNN-LSTM-BiGRU-based stress evaluation model shows superior accuracy compared to the other machine learning models (CNN-LSTM, CNN-GRU, LSTM, GRU, SVR, and BP) based on the MAPE, MAE, and RMSE. Moreover, the fish health classification under waterless and low-temperature conditions is thoroughly verified. High accuracy is proven by the classification validation criterion (accuracy, F1 score, precision, and recall). (4) Conclusions: the proposed health evaluation technology can precisely monitor and track the health status of live fish and provides an effective technical reference for the field of live fish vital sign detection. Full article
(This article belongs to the Special Issue Flexible and Wearable Sensors and Sensing for Agriculture and Food)
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Review

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21 pages, 9468 KiB  
Review
Optical Coherence Tomography as a Non-Invasive Tool for Plant Material Characterization in Agriculture: A Review
by Sm Abu Saleah, Shinheon Kim, Jannat Amrin Luna, Ruchire Eranga Wijesinghe, Daewoon Seong, Sangyeob Han, Jeehyun Kim and Mansik Jeon
Sensors 2024, 24(1), 219; https://doi.org/10.3390/s24010219 - 30 Dec 2023
Cited by 1 | Viewed by 1021
Abstract
Characterizing plant material is crucial in terms of early disease detection, pest control, physiological assessments, and growth monitoring, which are essential parameters to increase production in agriculture and prevent unnecessary economic losses. The conventional methods employed to assess the aforementioned parameters have several [...] Read more.
Characterizing plant material is crucial in terms of early disease detection, pest control, physiological assessments, and growth monitoring, which are essential parameters to increase production in agriculture and prevent unnecessary economic losses. The conventional methods employed to assess the aforementioned parameters have several limitations, such as invasive inspection, complexity, high time consumption, and costly features. In recent years, optical coherence tomography (OCT), which is an ultra-high resolution, non-invasive, and real-time unique image-based approach has been widely utilized as a significant and potential tool for assessing plant materials in numerous aspects. The obtained OCT cross-sections and volumetrics, as well as the amplitude signals of plant materials, have the capability to reveal vital information in both axial and lateral directions owing to the high resolution of the imaging system. This review discusses recent technological trends and advanced applications of OCT, which have been potentially adapted for numerous agricultural applications, such as non-invasive disease screening, optical signals-based growth speed detection, the structural analysis of plant materials, and microbiological discoveries. Therefore, this review offers a comprehensive exploration of recent advanced OCT technological approaches for agricultural applications, which provides insights into their potential to incorporate OCT technology into numerous industries. Full article
(This article belongs to the Special Issue Flexible and Wearable Sensors and Sensing for Agriculture and Food)
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