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Smart Sensors for Automation in Agriculture 4.0

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Published Papers

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

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 31861

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Special Issue Editors

Dr. Nick Sigrimis

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Guest Editor

Geosmart Automations Systems, CTO. Emeritus Professor of Agricultural University of Athens, School of Environment and Agricultural Engineering, X-Director of Laboratory of Farm Machinery and Automation, Athens, Greece
Interests: automation and electronics in agriculture; sensors; wireless sensor networks; precision agriculture; artificial intelligence; machine learning; smart sensors; edge computing and reinforcement learning; smart agriculture; Internet of Things; embedded intelligence; technology governance; Agriculture 4.0
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Minzan Li

E-Mail Website
Guest Editor

Research Center for Precision Agriculture, China Agricultural University, Beijing 100083, China
Interests: smart sensors for agriculture; soil and spectral sensors; greenhouse and hydroponic smart control
Special Issues, Collections and Topics in MDPI journals

Dr. Konstantinos G. Arvanitis

E-Mail Website
Guest Editor

Department of Natural Resources Development and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Street, 11855 Athens, Greece
Interests: process control; computational intelligence; automation in agriculture; wireless sensor networks; microgrids’ management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture 4.0 is a holistic transformation which occurs correspondingly to a similar evolution in the industrial sector and is attributed to the concept of Industry 4.0. In this context, Agriculture 4.0 rests on the entire automation of all agricultural operations through the seamless integration of smart sensing technologies to remotely collect, manage, and exchange real-time data related to environmental factors, soil conditions, and crop features. Beyond this sensing level, the true potential of Agriculture 4.0, in terms of productivity increase and quality optimization, resides in the ability to introduce advanced machine learning and artificial intelligence techniques by performing predictive analytics. We analyze the large amount of heterogeneous sensory data and aggregate them in order to facilitate smart sensing for the automation and sustainable development of the entire agricultural production domain, including crops, livestock, fisheries, and forestry.

The sustainable development of the agricultural sector is important for securing enough and healthy food for people. Our aim is to foster an environment that encourages publishing innovative ideas on smart sensing that provide the intelligence required to achieve what sustainable development requirements demand. Thus, this issue will publish works that stimulate and promote progress on “speaking plants and animals” that will attract exploitation actions and have a substantial impact on the domain with many inferential attributes. If you are interested in contributing a paper, please let us know the title as soon as possible. The final deadline for submissions is 30 September, 2021, though papers will be published on an ongoing basis within less than two months after submission.

This Special Issue aims to present recent advances in smart sensing technologies and their applications for automating agricultural production processes in accordance with the objectives for the operative implementation of Agriculture 4.0. For this purpose, original, high-quality research articles and review papers including recent research and developments in, but not limited to, the following areas are expected:

General topics

Design and implementation of novel agricultural smart sensing devices
Multimodal sensing techniques with data synchronization
Creating agricultural datasets and benchmarks
Novel applications of smart sensing for agriculture
From IIOT to AgIOT and technology governance
Multisensor systems, sensor fusion
Sensor networks in agriculture, wearable sensors, and the Internet of Things
Self-calibration of agricultural smart sensors
Deep learning from sensor data in agriculture

More specifics

Circuits or techniques for irrigation monitoring and control
Smart sensing for soil and water supply monitoring
Smart sensing for agricultural equipment monitoring and control
Smart sensing for greenhouse and hydroponics automation systems
Smart sensing for crop monitoring
Smart sensing for detection of microorganism and pest management
Smart sensing for minimizing food deficit and optimizing farming productivity
Smart sensing for the agriculture supply chain
Smart sensing for livestock monitoring
Optical sensors: hyperspectral, multispectral, fluorescence, and thermal sensing
Sensors for crop health status determination
Sensors and algorithms for crop phenotyping, germination, emergence, and determination of different growth stages of crops
Airborne sensors (UAV)
Non-destructive soil sensing
Yield estimation and prediction
Detection and identification of crops and weeds and weed control
Sensors for detection of fruits, fruit quality determination, and harvesting
Volatile components detection, electronic noses, and tongues
Low energy, disposable, and energy-harvesting sensors in agriculture
Sensors for robot navigation, localization, and mapping and environmental awareness
Sensors for robotic applications in crop management

Sensors for positioning, navigation, and obstacle detection

Dr. Nick Sigrimis
Prof. Dr. Minzan Li
Prof. Dr. Konstantinos Arvanitis
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 (9 papers)

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Research

Jump to: Review

17 pages, 2467 KiB

Open AccessArticle

Capability of the TrueColor Sensor Array for Determining the Nitrogen Supply in Winter Barley (Hordeum vulgare L.)

by Andreas Christ, Oliver Schmittmann and Peter Schulze Lammers

Sensors 2022, 22(16), 6032; https://doi.org/10.3390/s22166032 - 12 Aug 2022

Viewed by 1403

Abstract

In agriculture, efforts are being made to reduce pesticides and fertilizers because of the possible negative environmental impacts, high costs, political requirements, and declining social acceptance. With precision farming, significant savings can be achieved by the site-specific application of fertilizers. In contrast to currently available single sensors and camera-based systems, arrays or line sensors provide a suitable spatial resolution without requiring complex signal processing and promise significant potential regarding price and precision. Such systems comprise a cost-effective and compact unit that can be extended to any working width by cascading into arrays. In this study, experiments were performed to evaluate the applicability of a TrueColor sensor array in monitoring the nitrogen supply of winter barley during its growth. This sensor is based on recording the reflectance values in various channels of the CIELab color space: luminosity, green–red, and blue–yellow. The unique selling point of this sensor is the detection of luminosity because only the CIELab color space provides this opportunity. Strong correlations were found between the different reflection channels and the nitrogen level (R² = 0.959), plant coverage (R² = 0.907), and fresh mass yield (R² = 0.866). The fast signal processing allows this sensor to meet stringent demands for the operating speed, spatial resolution, and price structure. Full article

(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)

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14 pages, 4724 KiB

Open AccessArticle

Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling

by Sigfredo Fuentes, Claudia Gonzalez Viejo, Chelsea Hall, Yidan Tang and Eden Tongson

Sensors 2021, 21(21), 7312; https://doi.org/10.3390/s21217312 - 03 Nov 2021

Cited by 6 | Viewed by 1617

Abstract

Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near-infrared spectroscopy (NIR) and machine learning (ML) models. This research tested the NIR and ML approach and developed supervised regression ML models using Shiraz and Chardonnay berries and wines from a vineyard located in Yarra Valley, Victoria, Australia. An ML model was developed using NIR measurements from intact berries as inputs to estimate berry cell death (BCD), living tissue (LT) (Model 1). Furthermore, canopy architecture parameters obtained from cover photography of grapevine canopies and computer vision analysis were also tested as inputs to develop ML models to assess BCD and LT (Model 2) and the intensity of sensory descriptors based on visual and aroma profiles of wines for Chardonnay (Model 3) and Shiraz (Model 4). The results showed high accuracy and performance of models developed based on correlation coefficient (R) and slope (b) (M1: R = 0.87; b = 0.82; M2: R = 0.98; b = 0.93; M3: R = 0.99; b = 0.99; M4: R = 0.99; b = 1.00). Models developed based on canopy architecture, and computer vision can be used to automatically estimate the vigor and berry and wine quality traits using proximal remote sensing and with visible cameras as the payload of unmanned aerial vehicles (UAV). Full article

(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)

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22 pages, 580 KiB

Open AccessArticle

Processing Complex Events in Fog-Based Internet of Things Systems for Smart Agriculture

by Sandy F. da Costa Bezerra, Airton S. M. Filho, Flavia C. Delicato and Atslands R. da Rocha

Sensors 2021, 21(21), 7226; https://doi.org/10.3390/s21217226 - 30 Oct 2021

Cited by 10 | Viewed by 2271

Abstract

The recent growth of the Internet of Things’ services and applications has increased data processing and storage requirements. The Edge computing concept aims to leverage the processing capabilities of the IoT and other devices placed at the edge of the network. One embodiment of this paradigm is Fog computing, which provides an intermediate and often hierarchical processing tier between the data sources and the remote Cloud. Among the major benefits of this concept, the end-to-end latency can be decreased, thus favoring time-sensitive applications. Moreover, the data traffic at the network core and the Cloud computing workload can be reduced. Combining the Fog computing paradigm with Complex Event Processing (CEP) and data fusion techniques has excellent potential for generating valuable knowledge and aiding decision-making processes in the Internet of Things’ systems. In this context, we propose a multi-tier complex event processing approach (sensor node, Fog, and Cloud) that promotes fast decision making and is based on information with 98% accuracy. The experiments show a reduction of 77% in the average time of sending messages in the network. In addition, we achieved a reduction of 82% in data traffic. Full article

(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)

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11 pages, 2673 KiB

Open AccessCommunication

Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley

by Piotr Boniecki, Barbara Raba, Agnieszka A. Pilarska, Agnieszka Sujak, Maciej Zaborowicz, Krzysztof Pilarski and Dawid Wojcieszak

Sensors 2021, 21(17), 5696; https://doi.org/10.3390/s21175696 - 24 Aug 2021

Cited by 6 | Viewed by 1746

Abstract

Image analysis using neural modeling is one of the most dynamically developing methods employing artificial intelligence. The feature that caused such widespread use of this technique is mostly the ability of automatic generalization of scientific knowledge as well as the possibility of parallel analysis of the empirical data. A properly conducted learning process of artificial neural network (ANN) allows the classification of new, unknown data, which helps to increase the efficiency of the generated models in practice. Neural image analysis is a method that allows extracting information carried in the form of digital images. The paper focuses on the determination of imperfections such as contaminations and damages in the malting barley grains on the basis of information encoded in the graphic form represented by the digital photographs of kernels. This choice was dictated by the current state of knowledge regarding the classification of contamination that uses undesirable features of kernels to exclude them from use in the malting industry. Currently, a qualitative assessment of kernels is carried by malthouse-certified employees acting as experts. Contaminants are separated from a sample of malting barley manually, and the percentages of previously defined groups of contaminations are calculated. The analysis of the problem indicates a lack of effective methods of identifying the quality of barley kernels, such as the use of information technology. There are new possibilities of using modern methods of artificial intelligence (such as neural image analysis) for the determination of impurities in malting barley. However, there is the problem of effective compression of graphic data to a form acceptable for ANN simulators. The aim of the work is to develop an effective procedure of graphical data compression supporting the qualitative assessment of malting barley with the use of modern information technologies. Image analysis can be implemented into dedicated software. Full article

(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)

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27 pages, 13060 KiB

Open AccessArticle

Design and Validation of Computerized Flight-Testing Systems with Controlled Atmosphere for Studying Flight Behavior of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier)

by Maged Mohammed, Hamadttu El-Shafie and Nashi Alqahtani

Sensors 2021, 21(6), 2112; https://doi.org/10.3390/s21062112 - 17 Mar 2021

Cited by 11 | Viewed by 2565

Abstract

Understanding the flight characteristics of insect pests is essential for designing effective strategies and programs for their management. In this study, we designed, constructed, and validated the performance of modern flight-testing systems (flight mill and flight tunnel) for studying the flight behavior of red palm weevil (RPW) Rhynchophorus ferrugineus (Olivier) under a controlled atmosphere. The flight-testing mill consisted of a flight mill, a testing chamber with an automatically controlled microclimate, and a data logging and processing unit. The data logging and processing unit consisted of a USB digital oscilloscope connected with a laptop. We used MATLAB 2020A to implement a graphical user interface (GUI) for real-time sampling and data processing. The flight-testing tunnel was fitted with a horizontal video camera to photograph the insects during flight. The program of Image-Pro plus V 10.0.8 was used for image processing and numerical data analysis to determine weevil tracking. The mean flight speed of RPW was 82.12 ± 8.5 m/min, and the RPW stopped flying at the temperature of 20 °C. The RPW flight speed in the flight tunnel was slightly higher than that on the flight mill. The angular deceleration was 0.797 rad/s², and the centripetal force was 0.0203 N when a RPW tethered to the end of the rotating arm. The calculated moment of inertia of the RPW mass and the flight mill’s rotating components was 9.521 × 10⁻³ N m². The minimum thrust force needed to rotate the flight mill was 1.98 × 10⁻³ N. Therefore, the minimum power required to rotate the flight mill with the mean revolution per min of 58.02 rpm was approximately 2.589 × 10⁻³ W. The designed flight-testing systems and their applied software proved productive and useful tools in unveiling essential flight characteristics of test insects in the laboratory. Full article

(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)

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21 pages, 7320 KiB

Open AccessArticle

An Adaptive Roller Speed Control Method Based on Monitoring Value of Real-Time Seed Flow Rate for Flute-Roller Type Seed-Metering Device

by Wei Liu, Jianping Hu, Xingsheng Zhao, Mengjiao Yao, Imran Ali Lakhiar, Jun Zhao, Jiaxin Liu and Wei Wang

Sensors 2021, 21(1), 80; https://doi.org/10.3390/s21010080 - 25 Dec 2020

Cited by 7 | Viewed by 2981

Abstract

In order to obtain desirable crop yields, grain seeds need to be sown at the optimal seed amount per hectare with uniform distribution in the field. In previous grain sowing processes, the seeding rates are controlled by the rotational speed of the flute roller which significantly effects the uniform distribution of the seeds due to disturbances, such as the reduction of the seeds’ mass in the hopper and the change of working length of the flute roller. In order to overcome the above problem, we developed an adaptive roller speed control system based on the seed flow rate sensor. The developed system can monitor and feedback actual seeding rates. In addition, based on the monitoring value of the real-time seeding rates, an adaptive roller speed control method (ARSCM), which contains an algorithm for calculating the seeding rate with a compensation, was proposed. Besides, the seeding performance of the ARSCM and that of the conventional roller speed control method (CRSCM) were compared. The results of constant-velocity experiments demonstrated that the accuracy (SA) and the coefficient of variation (SCV) of the seeding rates controlled by the ARSCM were 94.12% and 6.77%, respectively. As for the CRSCM, the SA and SCV were 89.00% and 8.95%, respectively. Under variable-velocity conditions, the SA and SCV of the proposed system were 91.58% and 11.08%, respectively, while those of the CRSCM were 88.48% and 13.08%, respectively. Based on the above results, this study concluded that the ARSCM is able to replace the CRSCM in practical sowing processes for the optimal and uniform seed distribution in the field. Full article

(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)

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31 pages, 10935 KiB

Open AccessArticle

IoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato

by Sayed Moin-eddin Rezvani, Hamid Zare Abyaneh, Redmond R. Shamshiri, Siva K. Balasundram, Volker Dworak, Mohsen Goodarzi, Muhammad Sultan and Benjamin Mahns

Sensors 2020, 20(22), 6474; https://doi.org/10.3390/s20226474 - 12 Nov 2020

Cited by 25 | Viewed by 4813

Abstract

Optimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season. Full article

(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)

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Review

Jump to: Research

28 pages, 4678 KiB

Open AccessReview

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

by Michele Kremer Sott, Leandro da Silva Nascimento, Cristian Rogério Foguesatto, Leonardo B. Furstenau, Kadígia Faccin, Paulo Antônio Zawislak, Bruce Mellado, Jude Dzevela Kong and Nicola Luigi Bragazzi

Sensors 2021, 21(23), 7889; https://doi.org/10.3390/s21237889 - 26 Nov 2021

Cited by 23 | Viewed by 5244

Abstract

The agriculture sector is one of the backbones of many countries’ economies. Its processes have been changing to enable technology adoption to increase productivity, quality, and sustainable development. In this research, we present a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, so-called Digital Agriculture. To do this, we used 4694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis of the literature using SciMAT software with the support of the PICOC protocol. Our findings presented 22 strategic themes related to Digital Agriculture, such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAV) and Climate-smart Agriculture (CSA), among others. The thematic network structure of the nine most important clusters (motor themes) was presented and an in-depth discussion was performed. The thematic evolution map provides a broad perspective of how the field has evolved over time from 1994 to 2020. In addition, our results discuss the main challenges and opportunities for research and practice in the field of study. Our findings provide a comprehensive overview of the main themes related to Digital Agriculture. These results show the main subjects analyzed on this topic and provide a basis for insights for future research. Full article

(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)

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31 pages, 3872 KiB

Open AccessEditor’s ChoiceReview

Sensors for Structural Health Monitoring of Agricultural Structures

by Chrysanthos Maraveas and Thomas Bartzanas

Sensors 2021, 21(1), 314; https://doi.org/10.3390/s21010314 - 05 Jan 2021

Cited by 22 | Viewed by 7108

Abstract

The health diagnosis of agricultural structures is critical to detecting damages such as cracks in concrete, corrosion, spalling, and delamination. Agricultural structures are susceptible to environmental degradation due to frequent exposure to water, organic effluent, farm chemicals, structural loading, and unloading. Various sensors have been employed for accurate and real-time monitoring of agricultural building structures, including electrochemical, ultrasonic, fiber-optic, piezoelectric, wireless, fiber Bragg grating sensors, and self-sensing concrete. The cost–benefits of each type of sensor and utility in a farm environment are explored in the review. Current literature suggests that the functionality of sensors has improved with progress in technology. Notable improvements made with the progress in technology include better accuracy of the measurements, reduction of signal-to-noise ratio, and transmission speed, and the deployment of machine learning, deep learning, and artificial intelligence in smart IoT-based agriculture. Key challenges include inconsistent installation of sensors in farm structures, technical constraints, and lack of support infrastructure, awareness, and preference for traditional inspection methods. Full article

(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)

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