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Special Issue "IoT Technologies and the Agricultural Value Chain"

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

Deadline for manuscript submissions: closed (10 May 2021).

Special Issue Editors

Prof. Dr. Francisco Rodríguez
E-Mail Website
Guest Editor
System engineering and Automatic Control, Department of Informatics, University of Almería, 04120 Almería, Spain
Interests: modeling; automatic control; and robotics techniques applied to agriculture; safe energy; and building comfort
Special Issues, Collections and Topics in MDPI journals
Dr. Jorge Antonio Sánchez-Molina
E-Mail Website
Guest Editor
System engineering and Automatic Control, Department of Informatics, University of Almería, 04120 Almería, Spain
Interests: control; greenhouse; modeling; climate; fertigation; CO2; Internet of Things; DSS, MaaS
Special Issues, Collections and Topics in MDPI journals
Dr. Cynthia Giagnocavo
E-Mail Website
Guest Editor
Department of Economy and Business, University of Almería, 04120 Almería, Spain
Interests: Sustainable agriculture; business models; change management; organisations; digitalisation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food and agribusiness form a $5 trillion global industry with continuous growth (McKinsey). In the EU, around 11 million farms produce agricultural products for processing by about 300,000 enterprises in the food and drink industry. Food processors sell their products through the 2.8 million enterprises within the food distribution and food service industry, which deliver food to the EU’s 500 million consumers (EU Eurostat). Agriculture, food, and related industries contributed $992 billion to U.S. gross domestic product (GDP) in 2015, a 5.5-percent share (USDA).

Large retail organizations are increasing their requirements concerning food safety and sustainability. Exporters are affected by the increasing supply chain transparency and traceability required by retailers and wholesalers. In addition, consumers increasingly want more information in relation to food they consume. At the same time, sustainable agriculture must be also part of this approach. Agricultural systems are characterized by a demand for intensive and optimal use of land, water, energy and inputs, increasing productivity, and improved product quality, all the while maintaining competitiveness and market value within a sustainability framework.

Agrifood value chains are complex systems, involving physical, chemical, and biological processes, taking place simultaneously, reacting to environmental factors with different response times and patterns, and characterized by multiple interactions, which have to be controlled to obtain the best results for the grower and all actors in the agricultural value chain.

Technology and data sharing can be essential tools in seeking solutions through the introduction of technology in each of the phases of the supply/value chain, creating relationships among the different steps based on transparency and product and process information. As well, through the mediation and use of publicly available data, it can leverage such agricultural data´s value and improve societal use.

IoT implementation presents some benefits to the different actors and activities along the supply and value chain in terms of improved resource use, less waste, better data access and sharing, synchronization, reduced storage and cost, and enhanced consumer information access. IoT allows the collection of information about all links in the value chain, connecting systems so as to allow an integrated, multidimensional view of farming and agrifood activities, enabling deeper understanding on how the whole ecosystem works. Intensive use of ICT involving a large amount of data, intelligent and soft or virtual sensors, control loops, communication networks, storage, cloud services, and optimization techniques help to improve all value chain links. Moreover, IoT systems ideally should be based on flexible and efficient production focusing on important aspects like social and environmental added value, through the improvement of air, soil, water and energy efficiency and quality and overall social–economic–environmental sustainability.

The food and farm supply chain is increasingly complex as it involves numerous activities in growing systems, production planning, handling, logistics, traceability, and markets. As such, new business models and value propositions are necessary to reflect and leverage improved processes and products and the overall transformation of agri-food activities through IoT.

Papers included in this Special Issue will address the following areas:

  • IoT systems development;
  • Precision farming;
  • System integration and interoperability;
  • Data models;
  • IoT networks;
  • Intelligent sensors;
  • IoT sensors;
  • Visualization interfaces;
  • Decision support systems and data-driven decisions;
  • Virtual–soft sensors;
  • IoT-based business models;
  • Data sharing;
  • IoT-based control systems;
  • Robotics

Prof. Dr. Francisco Rodríguez
Prof. Dr. Jorge Antonio Sánchez Molina
Prof. Dr. Cynthia Giagnocavo
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. 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 2400 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

  • Internet of Things
  • Precision farming
  • DSS
  • Modeling
  • Soft sensors
  • IoT business models
  • Interoperability
  • Data sharing
  • Data models
  • Automatic control
  • Intelligent sensors

Published Papers (7 papers)

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Article
Management of Fertigation in Horticultural Crops through Automation with Electrotensiometers: Effect on the Productivity of Water and Nutrients
Sensors 2021, 21(1), 190; https://doi.org/10.3390/s21010190 - 30 Dec 2020
Cited by 1 | Viewed by 759
Abstract
Water and nutrient requirements of horticultural crops are influenced by different factors such as: Type of crop, stage of development and production system. Although greenhouse horticultural crops are more efficient in the use of water and fertilizers compared to other production systems, it [...] Read more.
Water and nutrient requirements of horticultural crops are influenced by different factors such as: Type of crop, stage of development and production system. Although greenhouse horticultural crops are more efficient in the use of water and fertilizers compared to other production systems, it is necessary increase efficiency for which individualized fertigation strategies must be designed for each greenhouse. The automation of fertigation based on the level of soil moisture allows optimization of management. The objective of this work was to determine the influence of the activation command of fertigation with electrotensiometers and the characteristics of the greenhouse on the productivity of the crop and the efficiency of use of water and nutrients in a sweet pepper crop. The trial was developed in two greenhouses. Four treatments were studied, combination of who two-factor: Soil matric potential (SMP) (SMP−10: Automatic activation of irrigation to −10 kPa and SMP−20: Automatic activation of irrigation to −20 kPa) and greenhouse characteristics (G1 and G2). The nutritive solution applied was the same in all treatments. The yield and volume of water and nutrients applied were determined, calculating the productivity of the water (WP), as well as productivity the nutrients. The fertigation activation threshold of −10 kPa presented the best results, increasing the yield and conserving WP and nutrient productivity with respect to −20 kPa in both greenhouses. The automation of irrigation with electrotensiometers allowed the application of different volume of fertigation demanded by the crop in each greenhouse, equalizing the WP and nutrient productivity without producing drainage. The pepper crop in the greenhouse G1 presented greater vegetative development, higher yield and demanded a greater volume of fertigation than G2 regardless of the activation threshold. This was due to the fact that the soil matric potential after irrigation in greenhouse G1 was closer to zero, being able to conclude that not only the soil matric potential threshold of irrigation activation has an influence on crop, but also the potential registered after irrigation. Soil matric potentials closer to zero are more productive. Full article
(This article belongs to the Special Issue IoT Technologies and the Agricultural Value Chain)
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Article
Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT
Sensors 2020, 20(21), 6354; https://doi.org/10.3390/s20216354 - 07 Nov 2020
Cited by 4 | Viewed by 738
Abstract
Hydrometeorological data sets are usually incomplete due to different reasons (malfunctioning sensors, collected data storage problems, etc.). Missing data do not only affect the resulting decision-making process, but also the choice of a particular analysis method. Given the increase of extreme events due [...] Read more.
Hydrometeorological data sets are usually incomplete due to different reasons (malfunctioning sensors, collected data storage problems, etc.). Missing data do not only affect the resulting decision-making process, but also the choice of a particular analysis method. Given the increase of extreme events due to climate change, it is necessary to improve the management of water resources. Due to the solution of this problem requires the development of accurate estimations and its application in real time, this work present two contributions. Firstly, different gap-filling techniques have been evaluated in order to select the most adequate one for river stage series: (i) cubic splines (CS), (ii) radial basis function (RBF) and (iii) multilayer perceptron (MLP) suitable for small processors like Arduino or Raspberry Pi. The results obtained confirmed that splines and monolayer perceptrons had the best performances. Secondly, a pre-validating Internet of Things (IoT) device was developed using a dynamic seed non-linear autoregressive neural network (NARNN). This automatic pre-validation in real time was tested satisfactorily, sending the data to the catchment basin process center (CPC) by using remote communication based on 4G technology. Full article
(This article belongs to the Special Issue IoT Technologies and the Agricultural Value Chain)
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Article
Characterization and Differentiation between Olive Varieties through Electrical Impedance Spectroscopy, Neural Networks and IoT
Sensors 2020, 20(20), 5932; https://doi.org/10.3390/s20205932 - 20 Oct 2020
Cited by 1 | Viewed by 637
Abstract
Electrical impedance has shown itself to be useful in measuring the properties and characteristics of agri-food products: fruit quality, moisture content, the germination capacity in seeds or the frost-resistance of fruit. In the case of olives, it has been used to determine fat [...] Read more.
Electrical impedance has shown itself to be useful in measuring the properties and characteristics of agri-food products: fruit quality, moisture content, the germination capacity in seeds or the frost-resistance of fruit. In the case of olives, it has been used to determine fat content and optimal harvest time. In this paper, a system based on the System on Chip (SoC) AD5933 running a 1024-point discrete Fourier transform (DFT) to return the impedance value as a magnitude and phase and which, working together with two ADG706 analog multiplexers and an external programmable clock based on a synthesized DDS in a FPGA XC3S250E-4VQG100C, allows for the impedance measurement in agri-food products with a frequency sweep from 1 Hz to 100 kHz. This paper demonstrates how electrical impedance is affected by the temperature both in freshly picked olives and in those processed in brine and provides a way to characterize cultivars by making use of only the electrical impedance, neural networks (NN) and the Internet of Things (IoT), allowing information to be collected from the olive samples analyzed both on farms and in factories. Full article
(This article belongs to the Special Issue IoT Technologies and the Agricultural Value Chain)
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Article
Distributed Key Management to Secure IoT Wireless Sensor Networks in Smart-Agro
Sensors 2020, 20(8), 2242; https://doi.org/10.3390/s20082242 - 15 Apr 2020
Cited by 5 | Viewed by 1275
Abstract
With the deepening of the research and development in the field of embedded devices, the paradigm of the Internet of things (IoT) is gaining momentum. Its technology’s widespread applications increasing the number of connected devices constantly. IoT is built on sensor networks, which [...] Read more.
With the deepening of the research and development in the field of embedded devices, the paradigm of the Internet of things (IoT) is gaining momentum. Its technology’s widespread applications increasing the number of connected devices constantly. IoT is built on sensor networks, which are enabling a new variety of solutions for applications in several fields (health, industry, defense, agrifood and agro sectors, etc.). Wireless communications are indispensable for taking full advantage of sensor networks but implies new requirements in the security and privacy of communications. Security in wireless sensor networks (WSNs) is a major challenge for extending IoT applications, in particular those related to the smart-agro. Moreover, limitations on processing capabilities of sensor nodes, and power consumption have made the encryption techniques devised for conventional networks not feasible. In such scenario, symmetric-key ciphers are preferred for key management in WSN; key distribution is therefore an issue. In this work, we provide a concrete implementation of a novel scalable group distributed key management method and a protocol for securing communications in IoT systems used in the smart agro sector, based on elliptic curve cryptography, to ensure that information exchange between layers of the IoT framework is not affected by sensor faults or intentional attacks. In this sense, each sensor node executes an initial key agreement, which is done through every member’s public information in just two rounds and uses some authenticating information that avoids external intrusions. Further rekeying operations require just a single message and provide backward and forward security. Full article
(This article belongs to the Special Issue IoT Technologies and the Agricultural Value Chain)
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Article
Real-Time Monitoring System for Shelf Life Estimation of Fruit and Vegetables
Sensors 2020, 20(7), 1860; https://doi.org/10.3390/s20071860 - 27 Mar 2020
Cited by 7 | Viewed by 1958
Abstract
The control of the main environmental factors that influence the quality of perishable products is one of the main challenges of the food industry. Temperature is the main factor affecting quality, but other factors like relative humidity and gas concentrations (mainly C2 [...] Read more.
The control of the main environmental factors that influence the quality of perishable products is one of the main challenges of the food industry. Temperature is the main factor affecting quality, but other factors like relative humidity and gas concentrations (mainly C2H4, O2 and CO2) also play an important role in maintaining the postharvest quality of horticultural products. For this reason, monitoring such environmental factors is a key procedure to assure quality throughout shelf life and evaluate losses. Therefore, in order to estimate the quality losses that a perishable product can suffer during storage and transportation, a real-time monitoring system has been developed. This system can be used in all post-harvest steps thanks to its Wi-Fi wireless communication architecture. Several laboratory trials were conducted, using lettuce as a model, to determine quality-rating scales during shelf life under different storage temperature conditions. As a result, a multiple non-linear regression (MNLR) model is proposed relating the temperature and the maximum shelf life. This proposed model would allow to predict the days the commodities will reduce their theoretical shelf-life when an improper temperature during storage or in-transit occurs. The system, developed as a sensor-based tool, has been tested during several land transportation trips around Europe. Full article
(This article belongs to the Special Issue IoT Technologies and the Agricultural Value Chain)
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Article
Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis
Sensors 2020, 20(5), 1541; https://doi.org/10.3390/s20051541 - 10 Mar 2020
Cited by 3 | Viewed by 1791
Abstract
Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since [...] Read more.
Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a “boat”), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used. Full article
(This article belongs to the Special Issue IoT Technologies and the Agricultural Value Chain)
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Case Report
Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors
Sensors 2019, 19(20), 4381; https://doi.org/10.3390/s19204381 - 10 Oct 2019
Cited by 7 | Viewed by 1364
Abstract
Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was [...] Read more.
Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was firstly analyzed. Next, a pioneer study on the data-driven calibration of soil moisture sensor was investigated considering the impacts of temperature. Different data-driven models including the multivariate adaptive regression splines and the Gaussian process regression were applied into the development of the calibration method. To verify the efficacy of the proposed methods, tests on four commercial soil moisture sensors were conducted; these sensors belong to the frequency domain reflection (FDR) type. The numerical results demonstrate that the proposed methods can greatly improve the measurement accuracy for the investigated sensors. Full article
(This article belongs to the Special Issue IoT Technologies and the Agricultural Value Chain)
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