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Advances in the Use of Artificial Intelligence (AI)/Machine Learning (ML)...
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Advances in the Use of Artificial Intelligence (AI)/Machine Learning (ML) and IoT in the Primary Sector

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 16099

Special Issue Editors

Dr. Nikos Petrellis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patra, Greece
Interests: image processing; classification; segmentation; disease diagnosis; plant disease; image filtering; Embedded systems; mixed signal; Signal Processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nikolaos Voros

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patra, Greece
Interests: embedded systems; cyber-physical systems
Dr. Christos P. Antonopoulos

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patra, Greece
Interests: wireless sensor networks architectures and performance; cross-layer commination protocols; power optimization for wireless sensor networks; cyber physical systems; internet of things; embedded systems; network simulation; performance evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI), and Machine Learning (ML) more generally, have been used in a variety of fields of application. Images, as well as other signals generated by IoT sensors, can be efficiently processed using AI or ML techniques. In the primary sector, AI and ML can be used to process signals from cameras and IoT sensors for crop, livestock, as well as fish culture monitoring. Diseases can be diagnosed early, and harvesting time can be optimally scheduled to achieve the highest production efficiency with the lowest cost and the lowest environmental impact.

This Special Issue of Electronics will provide a forum for presenting novel AI and ML approaches on data captured by IoT sensors and cameras in primary sector applications. The images can be captured from mobile devices, UAVs, or underwater cameras. Original research articles and review papers are both welcome.

Topics of Interest of this Special Issue include, but are not limited to:

  • AI/ML in precision agriculture applications:
    • Crop monitoring;
    • Exploiting UAVs and aerial photographs in crop monitoring;
    • Maturity of fruit selection;
    • Plant disease recognition;
    • Robotics in precision agriculture;
    • Irrigation control;
    • Pollution monitoring.
  • AI/ML in livestock monitoring:
    • Animal activity;
    • Health monitoring;
    • Animal health certification;
    • Sensors for livestock.
  • AI/ML in fish cultures:
    • Fish biometric feature measurement;
    • Fish health monitoring;
    • Fish waste management;
    • Fish culture pollution monitoring;
    • Submarine vehicles for fish culture monitoring.

Dr. Nikos Petrellis
Prof. Dr. Nikolaos Voros
Dr. Christos P. Antonopoulos
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. Electronics 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

  • precision agriculture
  • plant disease
  • irregation
  • crop monitoring
  • livestock monitoring
  • fish monitoring
  • image processing
  • AI/ML for object recognition
  • shape alignment
  • pattern recognition

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

33 pages, 11426 KiB  
Article
Plant Disease Identification Using Machine Learning Algorithms on Single-Board Computers in IoT Environments
by George Routis, Marios Michailidis and Ioanna Roussaki
Electronics 2024, 13(6), 1010; https://doi.org/10.3390/electronics13061010 - 7 Mar 2024
Cited by 6 | Viewed by 2354
Abstract
This paper investigates the usage of machine learning (ML) algorithms on agricultural images with the aim of extracting information regarding the health of plants. More specifically, a custom convolutional neural network is trained on Google Colab using photos of healthy and unhealthy plants. [...] Read more.
This paper investigates the usage of machine learning (ML) algorithms on agricultural images with the aim of extracting information regarding the health of plants. More specifically, a custom convolutional neural network is trained on Google Colab using photos of healthy and unhealthy plants. The trained models are evaluated using various single-board computers (SBCs) that demonstrate different essential characteristics. Raspberry Pi 3 and Raspberry Pi 4 are the current mainstream SBCs that use their Central Processing Units (CPUs) for processing and are used for many applications for executing ML algorithms based on popular related libraries such as TensorFlow. NVIDIA Graphic Processing Units (GPUs) have a different rationale and base the execution of ML algorithms on a GPU that uses a different architecture than a CPU. GPUs can also implement high parallelization on the Compute Unified Device Architecture (CUDA) cores. Another current approach involves using a Tensor Processing Unit (TPU) processing unit carried by the Google Coral Dev TPU Board, which is an Application-Specific Integrated Circuit (ASIC) specialized for accelerating ML algorithms such as Convolutional Neural Networks (CNNs) via the usage of TensorFlow Lite. This study experiments with all of the above-mentioned devices and executes custom CNN models with the aim of identifying plant diseases. In this respect, several evaluation metrics are used, including knowledge extraction time, CPU utilization, Random Access Memory (RAM) usage, swap memory, temperature, current milli Amperes (mA), voltage (Volts), and power consumption milli Watts (mW). Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence (AI)/Machine Learning (ML) and IoT in the Primary Sector)
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26 pages, 14240 KiB  
Article
A Potent Fractional-Order Controller for Interconnected Multi-Source Microgrids
by Ioannis Moschos, Ioannis Mastoras and Constantinos Parisses
Electronics 2023, 12(19), 4152; https://doi.org/10.3390/electronics12194152 - 6 Oct 2023
Cited by 2 | Viewed by 1090
Abstract
Frequency deviations and the capability to cope with demand are two of the main problems in isolated or interconnected microgrids, especially with the increase in the penetration level of renewable energy sources. Those two problems call for new improved controllers and methods able [...] Read more.
Frequency deviations and the capability to cope with demand are two of the main problems in isolated or interconnected microgrids, especially with the increase in the penetration level of renewable energy sources. Those two problems call for new improved controllers and methods able to suppress frequency deviations while keeping a balance between supply and demand. This study focuses on the implementation of a filtered fractional-order PDF controller in series with a one plus fractional-order PI controller (FOPDF-(1+FOPI)) for the frequency regulation of three-area multi-source interconnected microgrids. The proposed controller is optimized via the coot optimization algorithm. The proposed microgrids incorporate various sustainable units, renewable energy sources and a hybrid energy storage system in each area. The microgrids consist solely of sustainable and renewable sources and aim to provide possible microgrid configurations for 100% sustainable microgrids, which could be farms or small communities. The proposed controller is compared with the PIDF, integer-order PDF-(1+PI), and FOTDF-(1+TI) controllers in various scenarios. The first scenario involved evaluating the proposed controller in an isolated microgrid, where it achieved the best ITAE value, outperforming the second best by 29.5%. The second scenario considered three-area interconnected microgrids without RES penetration. The results revealed that the FOPDF-(1+FOPI) controller reduced the settling time in area one by 79.13% and 52.26% compared to that of the PIDF and FOTDF-(1+TI) controllers. Next, RES penetration was introduced into each microgrid in the form of steps or varied changes. Subsequently, performance evaluation was conducted in the presence of a communication time delay and noise in the control channels. Finally, a robustness assessment was conducted for the proposed controller in the interconnected microgrids with respect to parameter uncertainties. The simulations showed a maximum deviation in the settling time and maximum overshoot in area 1 of 66.6% and 38.74%, respectively Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence (AI)/Machine Learning (ML) and IoT in the Primary Sector)
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29 pages, 6436 KiB  
Article
Fish Monitoring from Low-Contrast Underwater Images
by Nikos Petrellis, Georgios Keramidas, Christos P. Antonopoulos and Nikolaos Voros
Electronics 2023, 12(15), 3338; https://doi.org/10.3390/electronics12153338 - 4 Aug 2023
Cited by 4 | Viewed by 2556
Abstract
A toolset supporting fish detection, orientation, tracking and especially morphological feature estimation with high speed and accuracy, is presented in this paper. It can be exploited in fish farms to automate everyday procedures including size measurement and optimal harvest time estimation, fish health [...] Read more.
A toolset supporting fish detection, orientation, tracking and especially morphological feature estimation with high speed and accuracy, is presented in this paper. It can be exploited in fish farms to automate everyday procedures including size measurement and optimal harvest time estimation, fish health assessment, quantification of feeding needs, etc. It can also be used in an open sea environment to monitor fish size, behavior and the population of various species. An efficient deep learning technique for fish detection is employed and adapted, while methods for fish tracking are also proposed. The fish orientation is classified in order to apply a shape alignment technique that is based on the Ensemble of Regression Trees machine learning method. Shape alignment allows the estimation of fish dimensions (length, height) and the localization of fish body parts of particular interest such as the eyes and gills. The proposed method can estimate the position of 18 landmarks with an accuracy of about 95% from low-contrast underwater images where the fish can be hardly distinguished from its background. Hardware and software acceleration techniques have been applied at the shape alignment process reducing the frame processing latency to less than 0.5 us on a general purpose computer and less than 16 ms on an embedded platform. As a case study, the developed system has been trained and tested with several Mediterranean fish species in the category of seabream. A large public dataset with low-resolution underwater videos and images has also been developed to test the proposed system under worst case conditions. Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence (AI)/Machine Learning (ML) and IoT in the Primary Sector)
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16 pages, 1021 KiB  
Article
Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing
by Sandesh Achar, Nuruzzaman Faruqui, Md Whaiduzzaman, Albara Awajan and Moutaz Alazab
Electronics 2023, 12(8), 1892; https://doi.org/10.3390/electronics12081892 - 17 Apr 2023
Cited by 18 | Viewed by 2672
Abstract
Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. [...] Read more.
Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen cyber-physical security (CPS) with minimal human intervention. It is an approach based on human activity recognition (HAR), where GoogleNet–BiLSTM network hybridization has been used to recognize suspicious activities in the cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates machine vision at the IoT edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and the GoogleNet–BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security at only USD 4.29±0.29 per month. Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence (AI)/Machine Learning (ML) and IoT in the Primary Sector)
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25 pages, 4833 KiB  
Article
Analysis of Enrollment Criteria in Secondary Schools Using Machine Learning and Data Mining Approach
by Zain ul Abideen, Tehseen Mazhar, Abdul Razzaq, Inayatul Haq, Inam Ullah, Hisham Alasmary and Heba G. Mohamed
Electronics 2023, 12(3), 694; https://doi.org/10.3390/electronics12030694 - 30 Jan 2023
Cited by 15 | Viewed by 4042
Abstract
Out-of-school children (OSC) surveys are conducted annually throughout Pakistan, and the results show that the literacy rate is increasing gradually, but not at the desired speed. Enrollment campaigns and targets system of enrollment given to the schools required a valuable model to analyze [...] Read more.
Out-of-school children (OSC) surveys are conducted annually throughout Pakistan, and the results show that the literacy rate is increasing gradually, but not at the desired speed. Enrollment campaigns and targets system of enrollment given to the schools required a valuable model to analyze the enrollment criteria better. In existing studies, the research community mainly focused on performance evaluation, dropout ratio, and results, rather than student enrollment. There is a great need to develop a model for analyzing student enrollment in schools. In this proposed work, five years of enrollment data from 100 schools in the province of Punjab (Pakistan) have been taken. The significant features have been extracted from data and analyzed through machine learning algorithms (Multiple Linear Regression, Random Forest, and Decision Tree). These algorithms contribute to the future prediction of school enrollment and classify the school’s target level. Based on these results, a brief analysis of future registrations and target levels has been carried out. Furthermore, the proposed model also facilitates determining the solution of fewer enrollments in school and improving the literacy rate. Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence (AI)/Machine Learning (ML) and IoT in the Primary Sector)
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17 pages, 5532 KiB  
Article
Detection Method of Fry Feeding Status Based on YOLO Lightweight Network by Shallow Underwater Images
by Haihui Yang, Yinyan Shi and Xiaochan Wang
Electronics 2022, 11(23), 3856; https://doi.org/10.3390/electronics11233856 - 23 Nov 2022
Cited by 3 | Viewed by 1929
Abstract
Pellet feed is widely used in fry feeding, which cannot sink to the bottom in a short time, so most fries eat in shallow underwater areas. Aiming at the characteristics of fry feeding, we present herein a nondestructive and rapid detection method based [...] Read more.
Pellet feed is widely used in fry feeding, which cannot sink to the bottom in a short time, so most fries eat in shallow underwater areas. Aiming at the characteristics of fry feeding, we present herein a nondestructive and rapid detection method based on a shallow underwater imaging system and deep learning framework to obtain fry feeding status. Towards this end, images of fry feeding in shallow underwater areas and floating uneaten pellets were captured, following which they were processed to reduce noise and enhance data information. Two characteristics were defined to reflect fry feeding behavior, and a YOLOv4-Tiny-ECA network was used to detect them. The experimental results indicate that the network works well, with a detection speed of 108FPS and a model size of 22.7 MB. Compared with other outstanding detection networks, the YOLOv4-Tiny-ECA network is better, faster, and has stronger robustness in conditions of sunny, cloudy, and bubbles. It indicates that the proposed method can provide technical support for intelligent feeding in factory fry breeding with natural light. Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence (AI)/Machine Learning (ML) and IoT in the Primary Sector)
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