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Advanced IoT Technologies in Agriculture
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Advanced IoT Technologies in Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 12553

Special Issue Editors

Dr. Pedro Gonçalves

E-Mail Website
Guest Editor
Technology and Management School of Águeda, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: internet of things; network management; system administration; smart farm; animal monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Paulo Pedreiras

E-Mail Website
Guest Editor
Electronics, Telecommunications and Informatics Department, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: distributed real-time systems; industrial communications; real-time scheduling; real-time medium access control; dynamic quality-of-service management; industrial internet of things; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. António Monteiro

E-Mail Website
Guest Editor
Research Centre for Natural Resources, Environment and Society (CERNAS), Escola Superior Agrária, Instituto Politécnico de Viseu, P3500-606 Viseu, Portugal
Interests: small ruminant production; precision agriculture; sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Steady technology advancements are continuously fostering the Internet-of-Things (IoT), enabling a massive and unprecedented deployment of digital devices and services in a range of application domains that never ceases to increase. Agriculture is no exception, and IoT technologies are increasingly adopted, promoting the Digital Transformation of this sector with the promise of significant benefits, such as resource use optimization, higher production levels and profit, along with reductions in exploration costs.

The growing interest in the adoption of IoT in Agriculture is also pushed by important societal challenges, such as to respond to the need to feed the growing world population, which is expected to reach 9,7 billion people in 2050, increase food security, improve sustainability and energy efficiency usage, and minimize the impact of agricultural activities on the environment, to name just a few.

In recent years the agricultural sector has been receiving enormous attention from academic and business circles. Terms such as “Precision Agriculture”, “Smart Farming” and “Agriculture 4.0/5.0” were coined over time to describe the progressive adoption of an increasingly wider range of technologies, which nowadays include advanced digital sensors, positioning technologies (GNSS), Geographic Information Systems, IoT, big data analysis, cloud computing, Wireless Sensors Networks and autonomous robots.

This Special Issue aims to highlight the latest research results and advances on technologies relevant for the automation of agriculture and farming processes, commonly known as Smart Farming/Agriculture 4.0/Agriculture 5.0. Therefore, we welcome the submission of reviews, research papers and communications. We encourage the publication of experimental and theoretical results with as much detail as possible. As is often the case with Applied Sciences, there is no restriction on the length of the papers, and the full experimental details should be provided for the sake of reproducibility of the results.

Topics of interest fall under the scope of Smart Farming/Agriculture 4.0/Agriculture 5.0 and include, but are not limited to: (i) farming monitoring (ii) autonomous farming processes (iii) big data-based processes on farming (iv) Internet of Things technologies (v) cloud computing (vi) robotics (vii) Wireless Sensor Networks.

Prof. Dr. Paulo Pedreiras
Dr. Pedro Gonçalves
Prof. Dr. António Monteiro
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. Applied Sciences 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

  • smart harvesting, condicioning and conservation
  • farm monitoring
  • precision agriculture
  • machine learning
  • big-data
  • robotics
  • IoT technologies
  • machine vision

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Published Papers (5 papers)

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Editorial

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2 pages, 196 KiB  
Editorial
Foreword to the Special Issue on Advanced IoT Technologies in Agriculture
by Pedro Gonçalves, Paulo Pedreiras and António Monteiro
Appl. Sci. 2022, 12(19), 10102; https://doi.org/10.3390/app121910102 - 8 Oct 2022
Cited by 1 | Viewed by 1018
Abstract
In recent decades, the perception of the impact of humanity’s ecological footprint has changed dramatically; it is now widely recognized that natural resources are limited and sensitive, and that their indiscriminate use is unsustainable and deeply impacts the well-being of people, animals and [...] Read more.
In recent decades, the perception of the impact of humanity’s ecological footprint has changed dramatically; it is now widely recognized that natural resources are limited and sensitive, and that their indiscriminate use is unsustainable and deeply impacts the well-being of people, animals and plants [...] Full article
(This article belongs to the Special Issue Advanced IoT Technologies in Agriculture)

Research

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22 pages, 2070 KiB  
Article
Modification of Values for the Horizontal Force of Tillage Implements Estimated from the ASABE Form Using an Artificial Neural Network
by Naji Mordi Naji Al-Dosary, Abdulwahed M. Aboukarima, Saad A. Al-Hamed, Moamen F. Zayed, Samy A. Marey and Ahmed Kayad
Appl. Sci. 2023, 13(13), 7442; https://doi.org/10.3390/app13137442 - 23 Jun 2023
Viewed by 1323
Abstract
The famous empirical model for the horizontal force estimation of farm implements was issued by the American Society of Agricultural Biological Engineers (ASABE). It relies on information on soil texture through its soil texture adjustment parameter, which is called the Fi -parameter. The [...] Read more.
The famous empirical model for the horizontal force estimation of farm implements was issued by the American Society of Agricultural Biological Engineers (ASABE). It relies on information on soil texture through its soil texture adjustment parameter, which is called the Fi -parameter. The Fi-parameter is not measurable, and the geometry of the plow through the machine parameter values are not measurable; however, the tillage speed, implement width, and tillage depth are measurable. In this study, the Fi-parameter was calibrated using a regression technique based on a soil texture norm that combines the sand, silt, and clay contents of a soil with R2 of 0.703. A feed-forward artificial neural network (ANN) with a backpropagation algorithm for training purposes was established to estimate the modified values of the horizontal force based on four inputs: working field criterion, soil texture norm, initial soil moisture content, and the horizontal force (which was estimated by the ASABE standard using the new—Fi-parameter). Our developed ANN model had high values for the coefficient of determination (R2) and their values in the training, testing, and validation stages were 0.8286, 0.8175, and 0.8515, respectively that demonstrated the applicability for the prediction of the modified horizontal forces. An Excel spreadsheet was created using the weights of the established ANN model to estimate the values of the horizontal force of specific tillage implements, such as a disk, chisel, or moldboard plows. The Excel spreadsheet was tested using data for a moldboard plow; in addition, a good prediction of the required horizontal force with a percentage error of 10% was achieved. The developed Excel spreadsheet contributed toward a numerical method that can be used by agricultural engineers in the future. Furthermore, we also concluded that the equations presented in this study can be formulated by any of computer language to create a simulation program to predict the horizontal force requirements of a tillage implement. Full article
(This article belongs to the Special Issue Advanced IoT Technologies in Agriculture)
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11 pages, 1483 KiB  
Article
Flock Nocturnal Activity: Is There a Rotative Guard?
by Pedro Gonçalves, Mário Antunes, William Xavier and António Monteiro
Appl. Sci. 2022, 12(22), 11563; https://doi.org/10.3390/app122211563 - 14 Nov 2022
Cited by 4 | Viewed by 1513
Abstract
Animal activity during the night period is of enormous importance, since it represents approximately half of animals’ lives, and monitoring it during this period makes it possible to detect problems related to well-being and safety, and allows us to infer energy expenditure on [...] Read more.
Animal activity during the night period is of enormous importance, since it represents approximately half of animals’ lives, and monitoring it during this period makes it possible to detect problems related to well-being and safety, and allows us to infer energy expenditure on the basis of their activity level. The present study analyzes a sheep activity dataset created during the night period to validate non-invasive techniques of monitoring that can be used to infer energy expenditure at night and to detect abnormal nocturnal activity. The study allowed us to detect cyclic changes in activity during the night period, which is composed of inactive and active periods, and to identify sheep lying positions. The analysis of the joint activity of the flock allowed us to perceive a time lag in the rest cycles, which consisted of periods of activity of ewes undone between elements of the flock. Although it does not allow us to identify the components of the period of inactivity, since the method used does not monitor brain activity, the results allow us to confirm the cyclical character of the nocturnal activity of sheep that has been reported in the literature, as well as their typical posture when lying down. Although this is an exploratory application with a very small number of animals, the similarity between the results obtained and the results documented in the existing literature, which have mostly been obtained using invasive methods, is encouraging, and suggests it is possible to rely on activity monitoring processes based on inertial sensors. Full article
(This article belongs to the Special Issue Advanced IoT Technologies in Agriculture)
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14 pages, 7199 KiB  
Article
Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
by Bruno Cardoso, Catarina Silva, Joana Costa and Bernardete Ribeiro
Appl. Sci. 2022, 12(18), 9397; https://doi.org/10.3390/app12189397 - 19 Sep 2022
Cited by 8 | Viewed by 4670
Abstract
With the increase of smart farming in the agricultural sector, farmers have better control over the entire production cycle, notably in terms of pest monitoring. In fact, pest monitoring has gained significant importance, since the excessive use of pesticides can lead to great [...] Read more.
With the increase of smart farming in the agricultural sector, farmers have better control over the entire production cycle, notably in terms of pest monitoring. In fact, pest monitoring has gained significant importance, since the excessive use of pesticides can lead to great damage to crops, substantial environmental impact, and unnecessary costs both in material and manpower. Despite the potential of new technologies, pest monitoring is still done in a traditional way, leading to excessive costs, lack of precision, and excessive use of human labour. In this paper, we present an Internet of Things (IoT) network combined with intelligent Computer Vision (CV) techniques to improve pest monitoring. First, we propose to use low-cost cameras at the edge that capture images of pest traps and send them to the cloud. Second, we use deep neural models, notably R-CNN and YOLO models, to detect the Whitefly (WF) pest in yellow sticky traps. Finally, the predicted number of WF is analysed over time and results are accessible to farmers through a mobile app that allows them to visualise the pest in each specific field. The contribution is to make pest monitoring autonomous, cheaper, data-driven, and precise. Results demonstrate that, by combining IoT, CV technology, and deep models, it is possible to enhance pest monitoring. Full article
(This article belongs to the Special Issue Advanced IoT Technologies in Agriculture)
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Review

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13 pages, 302 KiB  
Review
Effect of Processing on Volatile Organic Compounds Formation of Meat—Review
by Iwona Wojtasik-Kalinowska, Arkadiusz Szpicer, Weronika Binkowska, Monika Hanula, Monika Marcinkowska-Lesiak and Andrzej Poltorak
Appl. Sci. 2023, 13(2), 705; https://doi.org/10.3390/app13020705 - 4 Jan 2023
Cited by 9 | Viewed by 2936
Abstract
Meat is a rich source of different volatile compounds. The final flavor of meat products depends on the raw material and processing parameters. Changes that occur in meat include pyrolysis of peptides and amino acids, degradation of sugar and ribonucleotides, Maillard’s and Strecker’s [...] Read more.
Meat is a rich source of different volatile compounds. The final flavor of meat products depends on the raw material and processing parameters. Changes that occur in meat include pyrolysis of peptides and amino acids, degradation of sugar and ribonucleotides, Maillard’s and Strecker’s reactions, lipid oxidation, degradation of thiamine and fats, as well as microbial metabolism. A review of the volatile compounds’ formation was carried out and divided into non-thermal and thermal processes. Modern and advanced solutions such as ultrasounds, pulsed electric field, cold plasma, ozone use, etc., were described. The article also concerns the important issue of determining Volatile Organic Compounds (VOCs) markers generated during heat treatment. Full article
(This article belongs to the Special Issue Advanced IoT Technologies in Agriculture)
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