Special Issue "Applications of Sensor Technology to Agri-Food Systems"

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 15 October 2021.

Special Issue Editors

Dr. Dimitrios Argyropoulos
E-Mail Website
Guest Editor
School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland
Interests: drying; agricultural engineering; kinetic modeling; post harvest technology; medicinal plants and herbs; food processing; food quality; sensors; precision agriculture; drying technology
Special Issues and Collections in MDPI journals
Dr. Dimitrios S. Paraforos
E-Mail Website
Guest Editor
Institute of Agricultural Engineering, University of Hohenheim, Garben Str. 9, 70599 Stuttgart, Germany
Interests: agricultural machinery automation; ISOBUS technologies; unmanned ground and aerial vehicles; decentralized and resilient digital farming systems
Special Issues and Collections in MDPI journals
Prof. Dr. Spyros Fountas
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues, 

Data acquisition and associated means of automatic or semiautomatic identification are key sensor considerations in seeking effective solutions for resource use efficiency and food loss reduction within primary production and post-harvest handling of agricultural products along supply chains. In particular, new sensors are now available with reduced dimensions, reduced cost, and increased performance, which can be implemented and integrated into production systems, allowing an increase of data and eventually an increase of information. This is of great importance to support the digital transformation of agri-food systems, leading to the reduction of food loss on farm and the optimal use of production inputs. In order to exploit these results, authoritative studies associated with food production, harvesting, and postharvest handling practices are still needed to support the development and implementation of new solutions and best practices. This Special Issue will capture recent developments related to novel sensors and their proven or potential applications in the agri-food sector spanning fundamental scientific concepts, pilot use, and commercial sensing systems. Contributions are expected to deal with, but are not limited to, the following areas:

  • Soil, vegetation, air, and water sensors;
  • Sensors for determination of crop health status;
  • Monitoring of different growth stages of crops and phenotyping;
  • Early detection of diseases and pests;
  • Detection and identification of crops and weeds;
  • On the go sensing;
  • Non-destructive sensing;
  • Proximal and remote sensing;
  • Optical sensors and sensing systems;
  • Multispectral and hyperspectral sensors;
  • Fluorescence and thermal imaging;
  • Integration of sensors in agricultural machines;
  • Variable rate application;
  • Yield monitoring and mapping;
  • Multisensor systems, sensor fusion;
  • Sensors for detection of fruits and quality evaluation;
  • Wireless sensor networks;
  • IoT in agriculture and food sectors;
  • Smart sensor systems. 

Dr. Dimitrios Argyropoulos
Dr. Dimitrios S. Paraforos
Prof. Dr. Spyros Fountas
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 papers will be 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. Agriculture is an international peer-reviewed open access monthly 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 1600 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

  • Soil, vegetation, air, and water sensors
  • Sensors for determination of crop health status
  • Monitoring of different growth stages of crops and phenotyping
  • Early detection of diseases and pests
  • Detection and identification of crops and weeds
  • On the go sensing
  • Non-destructive sensing
  • Proximal and remote sensing
  • Optical sensors and sensing systems
  • Multispectral and hyperspectral sensors
  • Fluorescence and thermal imaging
  • Integration of sensors in agricultural machines
  • Variable rate application
  • Yield monitoring and mapping
  • Multisensor systems, sensor fusion
  • Sensors for detection of fruits and quality evaluation
  • Wireless sensor networks
  • IoT in agriculture and food sectors
  • Smart sensor systems

Published Papers (1 paper)

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Research

Article
Identification and Quantification of Olive Oil Quality Parameters Using an Electronic Nose
Agriculture 2021, 11(7), 674; https://doi.org/10.3390/agriculture11070674 - 16 Jul 2021
Viewed by 660
Abstract
An electronic nose (EN), which is a kind of chemical sensors, was employed to check olive oil quality parameters. Fifty samples of olive oil, covering the four quality categories extra virgin, virgin, ordinary virgin and lampante, were gathered from different Palestinian cities. The [...] Read more.
An electronic nose (EN), which is a kind of chemical sensors, was employed to check olive oil quality parameters. Fifty samples of olive oil, covering the four quality categories extra virgin, virgin, ordinary virgin and lampante, were gathered from different Palestinian cities. The samples were analysed chemically using routine tests and signals for each chemical were obtained using EN. Each signal acquisition represents the concentration of certain chemical constituents. Partial least squares (PLS) models were used to analyse both chemical and EN data. The results demonstrate that the EN was capable of modelling the acidity parameter with a good performance. The correlation coefficients of the PLS-1 model for acidity were 0.87 and 0.88 for calibration and validation sets, respectively. Furthermore, the values of the standard error of performance to standard deviation (RPD) for acidity were 2.61 and 2.68 for the calibration and the validation sets, respectively. It was found that two principal components (PCs) in the PLS-1 scores plot model explained 86% and 5% of EN and acidity variance, respectively. PLS-1 scores plot showed a high performance in classifying olive oil samples according to quality categories. The results demonstrated that EN can predict/model acidity with good precision. Additionally, EN was able to discriminate between diverse olive oil quality categories. Full article
(This article belongs to the Special Issue Applications of Sensor Technology to Agri-Food Systems)
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