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Agriculture
Special Issues
Applications of Sensor Technology to Agri-Food Systems
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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: closed (20 February 2022) | Viewed by 20137

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, Collections and Topics in MDPI journals
Prof. Dr. Dimitrios S. Paraforos

E-Mail Website
Guest Editor
Institut für Technik – Department of Agricultural Engineering, Hochschule Geisenheim University, Von-Lade-Str. 1, D-65366 Geisenheim, Germany
Interests: agricultural machinery automation; ISOBUS technologies; unmanned ground and aerial vehicles; decentralized and resilient digital farming systems
Special Issues, Collections and Topics in MDPI journals
Dr. Spyros Fountas

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
Interests: precision agriculture; UAV; information systems; farm machinery
Special Issues, Collections and Topics in MDPI journals

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 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. 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 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

  • 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

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Published Papers (5 papers)

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Research

14 pages, 8494 KiB  
Article
Position Accuracy Assessment of a UAV-Mounted Sequoia+ Multispectral Camera Using a Robotic Total Station
by Dimitrios S. Paraforos, Galibjon M. Sharipov, Andreas Heiß and Hans W. Griepentrog
Agriculture 2022, 12(6), 885; https://doi.org/10.3390/agriculture12060885 - 19 Jun 2022
Cited by 4 | Viewed by 2792
Abstract
Remote sensing data in agriculture that are originating from unmanned aerial vehicles (UAV)-mounted multispectral cameras offer substantial information in assessing crop status, as well as in developing prescription maps for site-specific variable rate applications. The position accuracy of the multispectral imagery plays an [...] Read more.
Remote sensing data in agriculture that are originating from unmanned aerial vehicles (UAV)-mounted multispectral cameras offer substantial information in assessing crop status, as well as in developing prescription maps for site-specific variable rate applications. The position accuracy of the multispectral imagery plays an important role in the quality of the final prescription maps and how well the latter correspond to the specific spatial characteristics. Although software products and developed algorithms are important in offering position corrections, they are time- and cost-intensive. The paper presents a methodology to assess the accuracy of the imagery obtained by using a mounted target prism on the UAV, which is tracked by a ground-based total station. A Parrot Sequoia+ multispectral camera was used that is widely utilized in agriculture-related remote sensing applications. Two sets of experiments were performed following routes that go along the north–south and east–west axes, while the cross-track error was calculated for all three planes, but also three-dimensional (3D) space. From the results, it was indicated that the camera’s D-GNSS receiver can offer imagery with a 3D position accuracy of up to 3.79 m, while the accuracy in the horizontal plane is higher compared to the vertical ones. Full article
(This article belongs to the Special Issue Applications of Sensor Technology to Agri-Food Systems)
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16 pages, 1814 KiB  
Article
Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge
by El Mehdi Ouafiq, Rachid Saadane and Abdellah Chehri
Agriculture 2022, 12(3), 329; https://doi.org/10.3390/agriculture12030329 - 24 Feb 2022
Cited by 37 | Viewed by 5097
Abstract
Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited [...] Read more.
Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited transferable data volumes. However, data management (DM) in intelligent agriculture is still not well understood due to the fact that there are not enough scientific publications available on this. Though data management (DM) benefits are factual and substantial, many challenges must be addressed in order to fully realize the DM’s potential. The main difficulties are data integration complexities, the lack of skilled personnel and sufficient resources, inadequate infrastructure, and insignificant data warehouse architecture. This work proposes a comprehensive architecture that includes big data technologies, IoT components, and knowledge-based systems. We proposed an AI-based architecture for smart farming. This architecture called, Smart Farming Oriented Big-Data Architecture (SFOBA), is designed to guarantee the system’s durability and the data modeling in order to transform the business needs for smart farming into analytics. Furthermore, the proposed solution is built on a pre-defined big data architecture that includes an abstraction layer of the data lake that handles data quality, following a data migration strategy in order to ensure the data’s insights. Full article
(This article belongs to the Special Issue Applications of Sensor Technology to Agri-Food Systems)
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18 pages, 7180 KiB  
Article
Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images
by Lifa Fang, Yanqiang Wu, Yuhua Li, Hongen Guo, Hua Zhang, Xiaoyu Wang, Rui Xi and Jialin Hou
Agriculture 2021, 11(12), 1190; https://doi.org/10.3390/agriculture11121190 - 25 Nov 2021
Cited by 11 | Viewed by 2990
Abstract
Consistent ginger shoot orientation helps to ensure consistent ginger emergence and meet shading requirements. YOLO v3 is used to recognize ginger images in response to the current ginger seeder’s difficulty in meeting the above agronomic problems. However, it is not suitable for direct [...] Read more.
Consistent ginger shoot orientation helps to ensure consistent ginger emergence and meet shading requirements. YOLO v3 is used to recognize ginger images in response to the current ginger seeder’s difficulty in meeting the above agronomic problems. However, it is not suitable for direct application on edge computing devices due to its high computational cost. To make the network more compact and to address the problems of low detection accuracy and long inference time, this study proposes an improved YOLO v3 model, in which some redundant channels and network layers are pruned to achieve real-time determination of ginger shoots and seeds. The test results showed that the pruned model reduced its model size by 87.2% and improved the detection speed by 85%. Meanwhile, its mean average precision (mAP) reached 98.0% for ginger shoots and seeds, only 0.1% lower than the model before pruning. Moreover, after deploying the model to the Jetson Nano, the test results showed that its mAP was 97.94%, the recognition accuracy could reach 96.7%, and detection speed could reach 20 frames·s−1. The results showed that the proposed method was feasible for real-time and accurate detection of ginger images, providing a solid foundation for automatic and accurate ginger seeding. Full article
(This article belongs to the Special Issue Applications of Sensor Technology to Agri-Food Systems)
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10 pages, 1580 KiB  
Article
Analysis of Fat and Protein Content in Milk Using Laser Polarimetric Scatterometry
by Alexey V. Shkirin, Dmitry N. Ignatenko, Sergey N. Chirikov, Nikolai F. Bunkin, Maxim E. Astashev and Sergey V. Gudkov
Agriculture 2021, 11(11), 1028; https://doi.org/10.3390/agriculture11111028 - 20 Oct 2021
Cited by 8 | Viewed by 3000
Abstract
Monitoring the composition of milk products is an important factor in the management of dairy farms and industry. Information on the quantitative content of milk components is necessary to control milk quality, as well as to optimize dairy cow nutrition and diagnose their [...] Read more.
Monitoring the composition of milk products is an important factor in the management of dairy farms and industry. Information on the quantitative content of milk components is necessary to control milk quality, as well as to optimize dairy cow nutrition and diagnose their clinical condition. The content of fat and protein is considered the main criterion for determining the market value of milk. Increasing the efficiency of dairy production requires the use of inexpensive and compact devices that are capable of performing multicomponent analysis of milk both directly on the farm and in technological lines. We investigated the possibility of fast simultaneous determination of fat and protein content in milk by laser polarimetric scatterometry. The block-diagonal elements of the scattering matrix were measured for a series of commercially produced milk samples with the indicated fat percentage, which were diluted by volume with water. From the measured scattering matrices, the size distributions of fat droplets and casein aggregates were reconstructed. Using the size histograms, the content of fat and protein and protein-to-fat ratio in the studied milk samples are estimated. Full article
(This article belongs to the Special Issue Applications of Sensor Technology to Agri-Food Systems)
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8 pages, 6358 KiB  
Article
Identification and Quantification of Olive Oil Quality Parameters Using an Electronic Nose
by Nawaf Abu-Khalaf
Agriculture 2021, 11(7), 674; https://doi.org/10.3390/agriculture11070674 - 16 Jul 2021
Cited by 17 | Viewed by 4141
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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