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Sensors for Food Supply Chain
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Sensors for Food Supply Chain

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

Deadline for manuscript submissions: closed (30 January 2023) | Viewed by 4294

Special Issue Editors

Prof. Dr. Marco Santonico

E-Mail Website
Guest Editor
Department of Science and Technology for Humans and the Environment, Campus Bio-Medico University of Rome, 00128 Rome, Italy
Interests: electronics for sensor systems; interfaces and integration of sensor systems and networks and their utilization in medical, food, and industrial applications
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Vollero

E-Mail Website
Guest Editor
Computer Systems and Bioinformatics Lab, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
Interests: digital signal processing; embedded systems; IoT systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industries and supply chains benefit from the continuous monitoring of their processes, and this is particularly true in the case of the food industry. The advantages are clear; thorough monitoring translates to better process control and analysis, which, in turn, enable improved management and quality of processes, resources, and products. Sensors, electronic interfaces, and networks of sensors play a primary role in this context, especially today and in conjunction with other IT paradigms such as IoT, IIoT, Big Data, and AI. Applications range all scales and markets, from small farms that produce quality products with niche markets, to big industries that transform products of different regions of the world and supply them to the global market. Moreover, sensors for the food supply chain enable new production and distribution paradigms such as precision food production, process, and distribution with significant impact on quality, costs, and sustainability. This Special Issue aims to collect contributions on sensors and their impact in all the scenarios related to the food industry and supply chain. 

Papers on the following and related topics are welcome: 

  • Smart packaging
  • Sensors for food quality control
  • Sensors for industrial applications
  • Sensors optimisation
  • Sensing materials for food applications
  • Sensor networks and supply chain applications
  • Food quality monitoring and control
  • Supply chain monitoring and control
  • VOCs detection
  • Ultra-low-power sensors

Prof. Dr. Marco Santonico
Dr. Luca Vollero
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

15 pages, 460 KiB  
Article
Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry
by Miguel Rivas Pellicer, Mohamed Yoosha Tungekar and Silvia Carpitella
Sensors 2023, 23(7), 3768; https://doi.org/10.3390/s23073768 - 06 Apr 2023
Cited by 2 | Viewed by 1559
Abstract
Industry 4.0 technologies offer manufacturing companies numerous tools to enhance their core processes, including monitoring and control. To optimize efficiency, it is crucial to effectively install monitoring sensors. This paper proposes a Multi-Criteria Decision-Making (MCDM) approach as a practical solution to the sensor [...] Read more.
Industry 4.0 technologies offer manufacturing companies numerous tools to enhance their core processes, including monitoring and control. To optimize efficiency, it is crucial to effectively install monitoring sensors. This paper proposes a Multi-Criteria Decision-Making (MCDM) approach as a practical solution to the sensor placement problem in the food industry, having been applied to wine bottling line equipment at a real Italian winery. The approach helps decision-makers when discriminating within a set of alternatives based on multiple criteria. By evaluating the interconnections within the different equipment, the ideal locations of sensors are suggested, with the goal of improving the process’s performance. The results indicated that the system of electric pumps, corker, conveyor, and capper had the most influence on the other equipment which are then recommended for sensor control. Monitoring this equipment will result in the early discovery of failures, potentially also involving other dependant equipment, contributing to enhance the level of performance for the whole bottling line. Full article
(This article belongs to the Special Issue Sensors for Food Supply Chain)
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15 pages, 3681 KiB  
Article
A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties
by Mikel Barrio-Conde, Marco Antonio Zanella, Javier Manuel Aguiar-Perez, Ruben Ruiz-Gonzalez and Jaime Gomez-Gil
Sensors 2023, 23(5), 2471; https://doi.org/10.3390/s23052471 - 23 Feb 2023
Cited by 3 | Viewed by 1895
Abstract
Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering [...] Read more.
Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds. Full article
(This article belongs to the Special Issue Sensors for Food Supply Chain)
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