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Selected Papers from the 2020 IEEE International Workshop on Metrology...
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Selected Papers from the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 20599

Special Issue Editors

Dr. Davide Brunelli

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Trento, I-38123 Trento, Italy
Interests: energy harvesting; ultra-low-power sensors; IoT; energy-neutral devices
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Annachiara Berardinelli

E-Mail Website
Guest Editor
Department of Industrial Engineering and Centre Agriculture Food Environment, University of Trento, Via Sommarive 9, Povo (Trento), Italy
Interests: innovative food processes; nonthermal treatments; food quality; nondestructive sensors; contactless sensors; data analysis; predictive models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (https://www.metroagrifor.org/) will be held in Trento, Italy, 4–6 November 2020. Authors of papers in topics of interest to Sensors who present at this workshop are invited to submit extended versions of their work to the related Special Issue for publication.

MetroAgriFor intends to create an active and stimulating forum where academics, researchers, and industry experts in the field of measurement and data processing techniques for agriculture, forestry, and food can meet and share new advances and research results. Attention is paid, but not limited to, new technologies for agriculture and forestry environment monitoring, food quality monitoring, metrology-assisted production in agriculture, forestry and food industries, sensors and associated signal conditioning for agriculture and forestry, calibration methods for electronic tests and measurement for environmental and food applications.

Topics:

  • Sensor networking and integration
  • Approaches and tools for measuring food quality
  • Soil analysis, mapping, and monitoring
  • Crop analysis, mapping, and monitoring
  • Precision agriculture, forestry and livestock farming
  • Measurements for agriculture, forestry, and environment
  • Agroclimatic measurements

Dr. Davide Brunelli
Dr. Annachiara Berardinelli
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 (4 papers)

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Research

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29 pages, 5300 KiB  
Article
Forecasting Air Temperature on Edge Devices with Embedded AI
by Gaia Codeluppi, Luca Davoli and Gianluigi Ferrari
Sensors 2021, 21(12), 3973; https://doi.org/10.3390/s21123973 - 09 Jun 2021
Cited by 19 | Viewed by 3700
Abstract
With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a [...] Read more.
With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402∘C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible. Full article
(This article belongs to the Special Issue Selected Papers from the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry)
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18 pages, 5985 KiB  
Article
Design and Implementation of an Energy-Efficient Weather Station for Wind Data Collection
by Padma Balaji Leelavinodhan, Massimo Vecchio, Fabio Antonelli, Andrea Maestrini and Davide Brunelli
Sensors 2021, 21(11), 3831; https://doi.org/10.3390/s21113831 - 01 Jun 2021
Cited by 7 | Viewed by 4555
Abstract
Agriculture faces critical challenges caused by changing climatic factors and weather patterns with random distribution. This has increased the need for accurate local weather predictions and weather data collection to support precision agriculture. The demand for uninterrupted weather stations is overwhelming, and the [...] Read more.
Agriculture faces critical challenges caused by changing climatic factors and weather patterns with random distribution. This has increased the need for accurate local weather predictions and weather data collection to support precision agriculture. The demand for uninterrupted weather stations is overwhelming, and the Internet of Things (IoT) has the potential to address this demand. One major challenge of energy constraint in remotely deployed IoT devices can be resolved using weather stations that are energy neutral. This paper focuses on optimizing the energy consumption of a weather station by optimizing the data collected and sent from the sensor deployed in remote locations. An asynchronous optimization algorithm for wind data collection has been successfully developed, using the development lifecyle specifically designed for weather stations and focused on achieving energy neutrality. The developed IoT weather station was deployed in the field, and it has the potential to reduce the power consumption of the weather station by more than 60%. Full article
(This article belongs to the Special Issue Selected Papers from the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry)
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21 pages, 4060 KiB  
Article
IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories
by Bruno Guilherme Martini, Gilson Augusto Helfer, Jorge Luis Victória Barbosa, Regina Célia Espinosa Modolo, Marcio Rosa da Silva, Rodrigo Marques de Figueiredo, André Sales Mendes, Luís Augusto Silva and Valderi Reis Quietinho Leithardt
Sensors 2021, 21(5), 1631; https://doi.org/10.3390/s21051631 - 26 Feb 2021
Cited by 29 | Viewed by 4220
Abstract
The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is [...] Read more.
The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use. Full article
(This article belongs to the Special Issue Selected Papers from the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry)
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Review

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23 pages, 1931 KiB  
Review
Sensing Technology for Fish Freshness and Safety: A Review
by Leonardo Franceschelli, Annachiara Berardinelli, Sihem Dabbou, Luigi Ragni and Marco Tartagni
Sensors 2021, 21(4), 1373; https://doi.org/10.3390/s21041373 - 16 Feb 2021
Cited by 41 | Viewed by 6755
Abstract
Standard analytical methods for fish freshness assessment are based on the measurement of chemical and physical attributes related to fish appearance, color, meat elasticity or texture, odor, and taste. These methods have plenty of disadvantages, such as being destructive, expensive, and time consuming. [...] Read more.
Standard analytical methods for fish freshness assessment are based on the measurement of chemical and physical attributes related to fish appearance, color, meat elasticity or texture, odor, and taste. These methods have plenty of disadvantages, such as being destructive, expensive, and time consuming. All these techniques require highly skilled operators. In the last decade, rapid advances in the development of novel techniques for evaluating food quality attributes have led to the development of non-invasive and non-destructive instrumental techniques, such as biosensors, e-sensors, and spectroscopic methods. The available scientific reports demonstrate that all these new techniques provide a great deal of information with only one test, making them suitable for on-line and/or at-line process control. Moreover, these techniques often require little or no sample preparation and allow sample destruction to be avoided. Full article
(This article belongs to the Special Issue Selected Papers from the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry)
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