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Special Issue "Smart Agricultural Applications with Internet of Things"

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

Deadline for manuscript submissions: closed (29 February 2020).

Special Issue Editors

Prof. Dr. Lei Shu
Website
Guest Editor
Dr. Sook Yoon
Website
Guest Editor
Department of Computer Engineering, Mokpo National University, Mokpo, Korea
Interests: image processing and computer vision; object recognition; machine learning; biometrics
Dr. Edmond Nurellari
Website
Guest Editor
School of Engineering, College of Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: distributed signal processing; signal processing on graphs; resource allocations and distributed decisions in wireless sensor networks
Dr. Kai Huang
Website
Guest Editor
Nanjing Agricultural University, China
Interests: agricultural Internet of things

Special Issue Information

Dear Colleague,

The third wave of the world’s information industry is coming. The Internet of things and smart agricultural technology have penetrated into various fields of agriculture, and a large number of new technologies, new products, new applications, and new models have emerged. As the focus of the government and enterprises in the agricultural field, the agricultural Internet of things and smart agricultural technology have made great progress.

In order to promote the development and application of smart agriculture and to better serve the modernization of agriculture and rural areas, it is necessary to improve the level of quantification, intelligence, and scientificization of agricultural production; promote the formation and development of industrial systems related to smart agriculture; and to modernize traditional agriculture, which will provide strong support for the transformation and upgrading of agriculture.

This Special Issue aims at providing a forum to present the latest advances on smart agriculture. Authors from both academia and agriculture are welcome to submit their original papers. Topics of interest include, but are not limited to, the following:

  • Agricultural artificial intelligence;
  • Agricultural blockchain;
  • Agricultural cloud computing and big data;
  • Agricultural Internet of things;
  • Agricultural knowledge engineering;
  • Agricultural remote sensing;
  • Agricultural robots and intelligent equipment;
  • Agricultural system simulation;
  • Precision agriculture.

The best papers from TeCrop 2019 (http://jlloret.webs.upv.es/tecrop2019/index.html) will be selected for the special issue.

Prof. Dr. Lei Shu
Dr. Sook Yoon
Dr. Edmond Nurellari
Dr. Kai Huang
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 2200 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 (12 papers)

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Research

Open AccessArticle
A Consortium Blockchain-Based Agricultural Machinery Scheduling System
Sensors 2020, 20(9), 2643; https://doi.org/10.3390/s20092643 - 06 May 2020
Cited by 2 | Viewed by 843
Abstract
The introduction of a consortium blockchain-based agricultural machinery scheduling system will help improve the transparency and efficiency of the data flow within the sector. Currently, the traditional agricultural machinery centralized scheduling systems suffer when there is a failure of the single point control [...] Read more.
The introduction of a consortium blockchain-based agricultural machinery scheduling system will help improve the transparency and efficiency of the data flow within the sector. Currently, the traditional agricultural machinery centralized scheduling systems suffer when there is a failure of the single point control system, and it also comes with high cost managing with little transparency, not leaving out the wastage of resources. This paper proposes a consortium blockchain-based agricultural machinery scheduling system for solving the problems of single point of failure, high-cost, low transparency, and waste of resources. The consortium blockchain-based system eliminates the central server in the traditional way, optimizes the matching function and scheduling algorithm in the smart contract, and improves the scheduling efficiency. The data in the system can be traced, which increases transparency and improves the efficiency of decision-making in the process of scheduling. In addition, this system adopts a crowdsourcing scheduling mode, making full use of idle agricultural machinery in the society, which can effectively solve the problem of resource waste. Then, the proposed system implements authentication access mechanisms, and allows only authorized users into the system. It includes transactions based on digital currency and eliminates third-party platform to charge service fees. Moreover, participating organizations have the opportunity to obtain benefits and reduce transaction costs. Finally, the upper layers supervision improves the efficiency and security of consensus algorithm, allows supervisors to block users with malicious motives, and always ensures system security. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes
Sensors 2020, 20(8), 2418; https://doi.org/10.3390/s20082418 - 24 Apr 2020
Cited by 5 | Viewed by 1154
Abstract
Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety [...] Read more.
Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
A Noise Tolerant Spread Spectrum Sound-Based Local Positioning System for Operating a Quadcopter in a Greenhouse
Sensors 2020, 20(7), 1981; https://doi.org/10.3390/s20071981 - 01 Apr 2020
Cited by 1 | Viewed by 858
Abstract
Quadcopters are beginning to play an important role in precision agriculture. In order to localize and operate the quadcopter automatically in complex agricultural settings, such as a greenhouse, a robust positioning system is needed. In previous research, we developed a spread spectrum sound-based [...] Read more.
Quadcopters are beginning to play an important role in precision agriculture. In order to localize and operate the quadcopter automatically in complex agricultural settings, such as a greenhouse, a robust positioning system is needed. In previous research, we developed a spread spectrum sound-based local positioning system (SSSLPS) with a 20 mm accuracy within a 30 × 30 m greenhouse area. In this research, a noise tolerant SSSLPS was developed and evaluated. First, the acoustic noise spectrum emitted by the quadcopter was documented, and then the noise tolerance properties of SSSounds were examined and tested. This was done in a greenhouse with a fixed quadcopter (9.75 N thrust) with the positioning system mounted on it. The recorded quadcopter noise had a broadband noise compared to the SSSound. Taking these SSSound properties into account, the noise tolerance of the SSSLPS was improved, achieving a positioning accuracy of 23.2 mm and 31.6 mm accuracy within 12 × 6 m for both Time-division Multiple Access (TDMA) and Frequency-division Multiple Access (FDMA) modulation. The results demonstrate that the SSSLPS is an accurate, robust positioning system that is noise tolerant and can used for quadcopter operation even within a small greenhouse. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud
Sensors 2020, 20(7), 1836; https://doi.org/10.3390/s20071836 - 26 Mar 2020
Cited by 1 | Viewed by 673
Abstract
The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential [...] Read more.
The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential task. It chooses physical sensors to create virtual sensors in response to the users’ requests. Considering the capricious meteorological environment of the outdoors, this paper presents an measurements similarity-based virtual-sensor provisioning scheme by taking advantage of machine learning in data analysis. First, to distinguish the changing trends, we classified all the physical sensors into several categories using historical data. Then, the k-means clustering algorithm was exploited for each class to cluster the physical sensors with high similarity. Finally, one representative physical sensor from each cluster was selected to create the corresponding virtual sensors. The experimental results show the reformation of our scheme with respect to energy efficiency, network lifetime, and data accuracy compared with the benchmark schemes. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
Measurement Method for Height-Independent Vegetation Indices Based on an Active Light Source
Sensors 2020, 20(7), 1830; https://doi.org/10.3390/s20071830 - 25 Mar 2020
Viewed by 654
Abstract
A coefficient CW, which was defined as the ratio of NIR (near infrared) to the red reflected spectral response of the spectrometer, with a standard whiteboard as the measuring object, was introduced to establish a method for calculating height-independent vegetation indices [...] Read more.
A coefficient CW, which was defined as the ratio of NIR (near infrared) to the red reflected spectral response of the spectrometer, with a standard whiteboard as the measuring object, was introduced to establish a method for calculating height-independent vegetation indices (VIs). Two criteria for designing the spectrometer based on an active light source were proposed to keep CW constant. A designed spectrometer, which was equipped with an active light source, adopting 730 and 810 nm as the central wavelength of detection wavebands, was used to test the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) in wheat fields with two nitrogen application rate levels (NARLs). Twenty test points were selected in each kind of field. Five measuring heights (65, 75, 85, 95, and 105 cm) were set for each test point. The mean and standard deviation of the coefficient of variation (CV) for NDVI in each test point were 3.85% and 1.39% respectively, the corresponding results for RVI were 2.93% and 1.09%. ANOVA showed the measured VIs possessed a significant ability to discriminate the NARLs and had no obvious correlation with the measurement heights. The experimental results verified the feasibility and validity of the method for measuring height-independent VIs. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
Sensors 2020, 20(7), 1827; https://doi.org/10.3390/s20071827 - 25 Mar 2020
Cited by 7 | Viewed by 1453
Abstract
The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use [...] Read more.
The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
An IoT Platform towards the Enhancement of Poultry Production Chains
Sensors 2020, 20(6), 1549; https://doi.org/10.3390/s20061549 - 11 Mar 2020
Cited by 2 | Viewed by 1308
Abstract
As a consequence of the projected world population growth, world meat consumption is expected to grow. Therefore, meat production needs to be improved, although it cannot be done at any cost. Maintaining the health and welfare status of animals at optimal levels has [...] Read more.
As a consequence of the projected world population growth, world meat consumption is expected to grow. Therefore, meat production needs to be improved, although it cannot be done at any cost. Maintaining the health and welfare status of animals at optimal levels has traditionally been a main concern of farmers, and more recently, consumers. In this article the Poultry Chain Management (PCM) platform is presented. It aims at collecting data across the different phases of the poultry production chain. The collection of this data not only contributes to determine the quality of each phase and the poultry production chain as a whole, but more importantly, to identify critical issues causing process inefficiencies and to support decision-making towards the holistic improvement of the production chain. Results showed that the information gathered can be exploited to make different suggestions to guarantee poultry welfare, and ultimately, improve the quality of the meat. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
Smart & Green: An Internet-of-Things Framework for Smart Irrigation
Sensors 2020, 20(1), 190; https://doi.org/10.3390/s20010190 - 29 Dec 2019
Cited by 4 | Viewed by 1582
Abstract
Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, [...] Read more.
Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, the data are not guaranteed to be free of discrepant values (i.e., outliers), which can damage the precision of irrigation management. Furthermore, data from different sources must fit into the same temporal window required for irrigation management and the data preprocessing must be dynamic and automatic to benefit users of the irrigation management plan. In this paper, we propose the Smart&Green framework to offer services for smart irrigation, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture. Outlier removal techniques allow for more precise irrigation management. For fields without soil moisture sensors, the prediction model estimates the matric potential using weather, crop, and irrigation information. We apply the predicted matric potential approach to the Van Genutchen model to determine the moisture used in an irrigation management scheme. We can save, on average, between 56.4% and 90% of the irrigation water needed by applying the Zscore, MZscore and Chauvenet outlier removal techniques to the predicted data. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval
Sensors 2019, 19(23), 5118; https://doi.org/10.3390/s19235118 - 22 Nov 2019
Cited by 2 | Viewed by 886
Abstract
As an artificial intelligence technique, case-based reasoning has considerable potential to build intelligent systems for smart agriculture, providing farmers with advice about farming operation management. A proper case representation method plays a crucial role in case-based reasoning systems. Some methods like textual, attribute-value [...] Read more.
As an artificial intelligence technique, case-based reasoning has considerable potential to build intelligent systems for smart agriculture, providing farmers with advice about farming operation management. A proper case representation method plays a crucial role in case-based reasoning systems. Some methods like textual, attribute-value pair, and ontological representations have been well explored by researchers. However, these methods may lead to inefficient case retrieval when a large volume of data is stored in the case base. Thus, an associated representation method is proposed in this paper for fast case retrieval. Each case is interconnected with several similar and dissimilar ones. Once a new case is reported, its features are compared with historical data by similarity measurements for identifying a relative similar past case. The similarity of associated cases is measured preferentially, instead of comparing all the cases in the case base. Experiments on case retrieval were performed between the associated case representation and traditional methods, following two criteria: the number of visited cases and retrieval accuracy. The result demonstrates that our proposal enables fast case retrieval with promising accuracy by visiting fewer past cases. In conclusion, the associated case representation method outperforms traditional methods in the aspect of retrieval efficiency. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
SensorTalk: An IoT Device Failure Detection and Calibration Mechanism for Smart Farming
Sensors 2019, 19(21), 4788; https://doi.org/10.3390/s19214788 - 04 Nov 2019
Cited by 9 | Viewed by 1215
Abstract
In an Internet of Things (IoT) system, it is essential that the data measured from the sensors are accurate so that the produced results are meaningful. For example, in AgriTalk, a smart farm platform for soil cultivation with a large number of sensors, [...] Read more.
In an Internet of Things (IoT) system, it is essential that the data measured from the sensors are accurate so that the produced results are meaningful. For example, in AgriTalk, a smart farm platform for soil cultivation with a large number of sensors, the produced sensor data are used in several Artificial Intelligence (AI) models to provide precise farming for soil microbiome and fertility, disease regulation, irrigation regulation, and pest regulation. It is important that the sensor data are correctly used in AI modeling. Unfortunately, no sensor is perfect. Even for the sensors manufactured from the same factory, they may yield different readings. This paper proposes a solution called SensorTalk to automatically detect potential sensor failures and calibrate the aging sensors semi-automatically. Numerical examples are given to show the calibration tables for temperature and humidity sensors. When the sensors control the actuators, the SensorTalk solution can also detect whether a failure occurs within a detection delay. Both analytic and simulation models are proposed to appropriately select the detection delay so that, when a potential failure occurs, it is detected reasonably early without incurring too many false alarms. Specifically, our selection can limit the false detection probability to be less than 0.7%. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System
Sensors 2019, 19(21), 4605; https://doi.org/10.3390/s19214605 - 23 Oct 2019
Cited by 5 | Viewed by 641
Abstract
Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar [...] Read more.
Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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Open AccessArticle
Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology
Sensors 2019, 19(18), 3918; https://doi.org/10.3390/s19183918 - 11 Sep 2019
Cited by 2 | Viewed by 903
Abstract
An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were [...] Read more.
An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094   s in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of 0.113   s . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of 140   ×   260 pixels could achieve high-precision vision navigation while the course deviation angle was not more than 7.5 ° . The maximum tractor speed of the optimal template and global template were 51.41   km / h and 27.47   km / h , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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