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Advanced Technologies in Agricultural Engineering and Energy Optimization

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 21119

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Special Issue Editors

Dr. Vadim Bolshev

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1. Head of Laboratory of Intelligent Agricultural Machines and Complexes, Don State Technical University, Rostov-on-Don 344000, Russia
2. Senior Scientific Officer, Laboratory of Power Supply and Heat Supply, Federal Scientific Agroengineering Center VIM, 109456 Moscow, Russia
Interests: agroengineering technologies; rural power supply; renewable energy; electric vehicles; smart agriculture
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Dr. Vladimir Panchenko

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1. Department of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, Russia
2. Laboratory of Systems of Non-Traditional Energy, Federal Scientific Agroengineering Center VIM, 109456 Moscow, Russia
Interests: renewable energy; agroengineering technologies; autonomous power supply; electric transport; three-dimensional modeling
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Dr. Nallapaneni Manoj Kumar

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Guest Editor

School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong
Interests: building-integrated photovoltaics; circular economy; sustainability and resilience; energy management; life cycle assessment; technoeconomic analysis; modeling and performance investigation of energy systems; solar for smart cities applications; advances in solar energy installations; microgrids; blockchain technology; Internet of Things
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Dr. Pandian Vasant

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Guest Editor

MERLIN Research Centre, Faculty of Electrical and Electronic Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
Interests: artificial intelligence; computing in mathematics; natural science; engineering and medicine algorithms
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Prof. Dr. Igor Litvinchev

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Guest Editor

Graduate Program in Systems Engineering, Nuevo Leon State University (UANL), Av. Universidad s/n, Col. Ciudad Universitaria, San Nicolas de los Garza 66455, Nuevo Leon, Mexico
Interests: modeling, optimization and control of large scale systems; optimization; operations research
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Prof. Dr. Prasun Chakrabarti

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Deputy Provost, ITM (SLS), Baroda University (ITMBU), Vadodara 391510, India
Interests: artificial intelligence in renewable energy; data analytics in power systems; medical informatics

Special Issue Information

Dear Colleagues,

The major population growth, along with its flow from rural areas into urban agglomerations, cause the problem of food shortages, becoming the primary determinants of the global poverty. Food shortages are also significantly exacerbated due to land degradation lowering agricultural yields and livestock productivity. Fortunately, a rapid development of technologies, including information technologies and renewable energy sources, is currently observed in many aspects of economy. The direction of research is towards the utilization of renewable energy, which could help fulfill the energy demand, as well as to mitigate environmental problems. The use of renewable energy plays an important role in agriculture, where technologies are also being improved from year to year. Agricultural production is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex computing, optimization, planning and working with large amounts of data. Nowadays, a large amount of unstructured heterogeneous data powers the demand to extract useful insights in an automatic, reliable and scalable way. The agriculture sector, however, is historically less receptive to innovation and lags behind the implementation of contemporary solutions, which defines the relevance of this Special Issue.

The purpose of this Special Issue “Advanced Technologies in Agricultural Engineering and Energy Optimization” is to publish research papers, as well as review articles, addressing recent advances on agriculture engineering within the confines of use of various energy types. This Special Issue aims to seek high-quality papers from academics and industry-related researchers in the areas of power supply to rural areas, biofuels and renewable energies used in agriculture, energy efficiency and conservation in agriculture, agricultural robotic applications, livestock production, the application of electrophysical impact on agricultural objects, technologies in harvesting and seed machinery, solutions for digital and precision agriculture, applied mathematics, environmental bioengineering, machine learning, artificial intelligence, pattern recognition, data mining, multimedia processing and big data to show the most recently advanced methods.

Dr. Vadim Bolshev
Dr. Vladimir Panchenko
Dr. Nallapaneni Manoj Kumar
Dr. Pandian Vasant
Prof. Dr. Igor Litvinchev
Prof. Dr. Prasun Chakrabarti
Guest Editors

Manuscript Submission Information

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Keywords

agricultural engineering
artificial intelligence
big data analytics, development and applications
bio-energies
cloud computing and deep learning
control systems
distributed energy sources
equipment and technologies for agriculture
energy efficiency in agriculture
environmental issues
Industry 4.0
Internet of Things
issues of energy supply and reliability computing
livestock production and management
microbiological research
modern crop and livestock issues
multiobjective optimization
postharvest technology
process optimization
renewable energy
robotics
rural electrification
rural microgrid
smart city and green information systems
smart farming
smart optimization
waste management and recycling

Published Papers (10 papers)

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Research

14 pages, 3461 KiB

Open AccessArticle

Landfill Waste Segregation Using Transfer and Ensemble Machine Learning: A Convolutional Neural Network Approach

by Angelika Sita Ouedraogo, Ajay Kumar and Ning Wang

Energies 2023, 16(16), 5980; https://doi.org/10.3390/en16165980 - 15 Aug 2023

Viewed by 1126

Abstract

Waste disposal remains a challenge due to land availability, and environmental and health issues related to the main disposal method, landfilling. Combining computer vision (machine learning) and robotics to sort waste is a cost-effective solution for landfilling activities limitation. The objective of this study was to combine transfer and ensemble learning to process collected waste images and classify landfill waste into nine classes. Pretrained CNN models (Inception–ResNet-v2, EfficientNetb3, and DenseNet201) were used as base models to develop the ensemble network, and three other single CNN models (Models 1, 2, and 3). The single network performances were compared to the ensemble model. The waste dataset, initially grouped in two classes, was obtained from Kaggle, and reorganized into nine classes. Classes with a low number of data were improved by downloading additional images from Google search. The Ensemble Model showed the highest prediction precision (90%) compared to the precision of Models 1, 2, and 3, 86%, 87%, and 88%, respectively. All models had difficulties predicting overlapping classes, such as glass and plastics, and wood and paper/cardboard. The environmental costs for the Ensemble network, and Models 2 and 3, approximately 15 g CO₂ equivalent per training, were lower than the 19.23 g CO₂ equivalent per training for Model 1. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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14 pages, 3236 KiB

Open AccessArticle

Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks

by Aniket Vatsa, Ananda Shankar Hati, Vadim Bolshev, Alexander Vinogradov, Vladimir Panchenko and Prasun Chakrabarti

Energies 2023, 16(5), 2382; https://doi.org/10.3390/en16052382 - 02 Mar 2023

Cited by 4 | Viewed by 1778

Abstract

Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a

R^{2}

value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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15 pages, 2986 KiB

Open AccessArticle

A Fractional Order Controller for Sensorless Speed Control of an Induction Motor

by Tayyaba Nosheen, Ahsan Ali, Muhammad Umar Chaudhry, Dmitry Nazarenko, Inam ul Hasan Shaikh, Vadim Bolshev, Muhammad Munwar Iqbal, Sohail Khalid and Vladimir Panchenko

Energies 2023, 16(4), 1901; https://doi.org/10.3390/en16041901 - 14 Feb 2023

Cited by 9 | Viewed by 1996

Abstract

Agriculture activities are completely dependent upon energy production worldwide. This research presents sensorless speed control of a three-phase induction motor aided with an extended Kalman filter (EKF). Although a proportional integral (PI) controller can ensure tracking of the rotor speed, a considerable magnitude of ripples is present in the torque generated by a motor. Adding a simple derivative to have a proportional integral derivative (PID) action can cause a further increase in ripple magnitude, as it allows the addition of high-frequency noise in the system. Therefore, a fractional-order-based PID control is presented. The proposed control scheme is applied in a closed loop with the system, and simulation results are compared with the PID controller. It is evident from the results that the fractional order control not only ensures 20 times faster tracking, but ripple magnitude in torque was also reduced by a factor of 50% compared to that while using PID and ensures the effectiveness of the proposed strategy. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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17 pages, 3848 KiB

Open AccessArticle

Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression

by Jahir Pasha Molla, Dharmesh Dhabliya, Satish R. Jondhale, Sivakumar Sabapathy Arumugam, Anand Singh Rajawat, S. B. Goyal, Maria Simona Raboaca, Traian Candin Mihaltan, Chaman Verma and George Suciu

Energies 2023, 16(1), 555; https://doi.org/10.3390/en16010555 - 03 Jan 2023

Cited by 13 | Viewed by 1907

Abstract

The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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15 pages, 4015 KiB

Open AccessFeature PaperArticle

Evaluation of Particulate Matter (PM) Emissions from Combustion of Selected Types of Rapeseed Biofuels

by Joanna Szyszlak-Bargłowicz, Jacek Wasilewski, Grzegorz Zając, Andrzej Kuranc, Adam Koniuszy and Małgorzata Hawrot-Paw

Energies 2023, 16(1), 239; https://doi.org/10.3390/en16010239 - 26 Dec 2022

Cited by 3 | Viewed by 1769

Abstract

The manuscript describes the results of an experimental study of the level of PM (particulate matter) emissions arising from the combustion of two selected types of biomass (i.e., rapeseed straw pellets and engine biofuel (biodiesel, FAME)), which were derived from rapeseed. The PM emissions from the combustion of biofuels were compared with those obtained from the combustion of their traditional counterparts (i.e., wood pellets and diesel fuel). Both types of pellets were burned in a 10 kW boiler designed to burn these types of fuels. The engine fuels tested were burned in a John Deere 4045TF285JD engine mounted on a dynamometer bench in an engine dyno, under various speed and load conditions. A Testo 380 analyzer was used to measure the PM emission levels in boiler tests, while an MPM4 particle emission meter was used in the engine tests. The combustion (under rated conditions) of rapeseed straw pellets resulted in a significant increase in PM emissions compared to the combustion of wood pellets. The PM emissions during the combustion of wood pellets were 15.45 mg·kg⁻¹, during the combustion of rapeseed straw pellets, they were 336 mg·kg⁻¹, and the calculated emission factors were 44.5 mg·MJ⁻¹ and 1589 mg·MJ⁻¹, respectively. In the engine tests, however, significantly lower particulate emissions were obtained for the evaluated biofuel compared to its conventional counterpart. The combustion of rapeseed oil methyl esters resulted in a 40–60% reduction in PM content in the exhaust gas on average for the realized engine speeds over the full load range compared to the combustion of diesel fuel. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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11 pages, 419 KiB

Open AccessArticle

Urban Sustainability: Recovering and Utilizing Urban Excess Heat

by Kristina Lygnerud and Sarka Langer

Energies 2022, 15(24), 9466; https://doi.org/10.3390/en15249466 - 14 Dec 2022

Cited by 3 | Viewed by 1728

Abstract

Urban heat sources from urban infrastructure and buildings could meet ~10% of the European building heating demand. There is, however, limited information on how to use them. The EU project ReUseHeat has generated much of the existing knowledge on urban waste heat recovery implementation. Heat recovery from a data center, hospital and from water were demonstrated. Additionally, the project generated knowledge of stakeholders, risk profile, bankability and business models. The recovery of urban waste heat is characterized by high potential, high competitiveness compared to other heating alternatives, high avoidance of GHG emissions, payback within three years and low utilization. These characteristics reveal that barriers for increased utilization exist. The barriers are not technical. Instead, the absence of a waste heat EU level policy adds risk. Other showstoppers are low knowledge on the urban waste heat opportunity and new stakeholder relationships being needed for successful recovery. By combining key results and lessons learned from the project this article outlines the frontier of urban waste heat recovery research and practice in 2022. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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14 pages, 2635 KiB

Open AccessArticle

Evaluation of the Effectiveness of Different LED Irradiators When Growing Red Mustard (Brassica juncea L.) in Indoor Farming

by Natalya A. Semenova, Alexandr A. Smirnov, Alexey S. Dorokhov, Yuri A. Proshkin, Alina S. Ivanitskikh, Narek O. Chilingaryan, Artem A. Dorokhov, Denis V. Yanykin, Sergey V. Gudkov and Andrey Yu. Izmailov

Energies 2022, 15(21), 8076; https://doi.org/10.3390/en15218076 - 31 Oct 2022

Cited by 4 | Viewed by 1847

Abstract

Investigation is devoted to the optimization of light spectrum and intensity used for red mustard growing. Notably, most of the studies devoted to red mustard growing were conducted on micro-greens, which is not enough for the development of methods and recommendations for making the right choices about the irradiation parameters for full-cycle cultivation. In this study, we tested four models of LED with different ratios of blue, green red and far red radiation intensity: 12:20:63:5; 15:30:49:6; 30:1:68:1, in two values of photon flux density (PFD)—120 and 180 µmol m⁻² s⁻¹—to determine the most effective combination for red mustard growing. The study was conducted in a container-type climate chamber, where the red leaf mustard was cultivated in hydroponics. On the 30th day of cultivation, the plant’s morphological, biochemical and chlorophyll fluorescence parameters, and reflection coefficients were recorded. The results indicated that the PFD 120 µmol m⁻² s⁻¹ had a worse effect on both mustard leaf biomass accumulation and nitrate concentration (13–30% higher) in the plants. The best lighting option for growing red mustard was the blue–red spectrum, as the most efficient in terms of converting electricity into biomass (77 Wth/g). This light spectrum contributes to plant development with a larger leaf area (60%) and a fresh mass (54%) compared with the control, which has a maximum similarity in spectrum percentage to the sunlight spectrum. The presence of green and far red radiation with the blue–red light spectrum in various proportions at the same level of PFD had a negative effect on plant fresh mass, leaf surface area and photosynthetic activity. The obtained results could be useful for lighting parameters’ optimization when growing red mustard in urban farms. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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14 pages, 2311 KiB

Open AccessArticle

Evaluation of Greenhouse Gas Emission Levels during the Combustion of Selected Types of Agricultural Biomass

by Jacek Wasilewski, Grzegorz Zając, Joanna Szyszlak-Bargłowicz and Andrzej Kuranc

Energies 2022, 15(19), 7335; https://doi.org/10.3390/en15197335 - 06 Oct 2022

Cited by 6 | Viewed by 1251

Abstract

This paper presents the results of an experimental study of the emission levels of selected greenhouse gases (CO₂, CH₄, NO_x) arising from the combustion of different forms of biomass, i.e., solid biomass in the form of pellets and liquid biomass in the example of engine biofuel (biodiesel). Both types of biomass under study are rape-based biofuels. The pellets are made from rape straw, which, as a waste product, can be used for energy purposes. Additionally, biodiesel contains rape oil methyl esters (FAME) designed to power diesel engines. The boiler 25 kW was used to burn the pellets. Engine measurements were performed on a dynamometer bench on an S-4003 tractor engine. An analyzer Testo 350 was used to analyze the exhaust gas. CO₂ emission studies do not indicate the environmental benefits of using any alternative fuels tested compared to their conventional counterparts. In both the engine and boiler tests for NO_x emissions, no environmental benefits were demonstrated from the use of alternative fuels. The measured average NO_x emission levels for biodiesel compared to diesel were about 20% higher, and for rapeseed straw pellets, they were more than 60% higher compared to wood pellets. Only in the case of engine tests was significantly lower CH₄ (approx. 30%) emission found when feeding the engine with rape oil methyl esters. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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26 pages, 9406 KiB

Open AccessArticle

Modeling and Control Strategy of Wind Energy Conversion System with Grid-Connected Doubly-Fed Induction Generator

by Abrar Ahmed Chhipą, Prąsun Chakrabarti, Vadim Bolshev, Tulika Chakrabarti, Gennady Samarin, Alexey N. Vasilyev, Sandeep Ghosh and Alexander Kudryavtsev

Energies 2022, 15(18), 6694; https://doi.org/10.3390/en15186694 - 13 Sep 2022

Cited by 16 | Viewed by 4320

Abstract

The most prominent and rapidly increasing source of electrical power generation, wind energy conversion systems (WECS), can significantly improve the situation with regard to remote communities’ power supply. The main constituting elements of a WECS are a wind turbine, a mechanical transmission system, a doubly-fed induction generator (DFIG), a rotor side converter (RSC), a common DC-link capacitor, and a grid-side converter. Vector control is center for RSC and GSC control techniques. Because of direct and quadrature components, the active and reactive power can also be controller precisely. This study tracks the maximum power point (MPP) using a maximum power point tracking (MPPT) controller strategy. The MPPT technique provides a voltage reference to control the maximum power conversion at the turbine end. The performance and efficiency of the suggested control strategy are validated by WECS simulation under fluctuating wind speed. The MATLAB/Simulink environment using simpower system toolbox is used to simulate the proposed control strategy. The results reveal the effectiveness of the proposed control strategy under fluctuating wind speed and provides good dynamic performance. The total harmonic distortions are also within the IEEE 519 standard’s permissible limits which is also an advantage of the proposed control approach. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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21 pages, 6249 KiB

Open AccessArticle

Deep Feature Based Siamese Network for Visual Object Tracking

by Su-Chang Lim, Jun-Ho Huh and Jong-Chan Kim

Energies 2022, 15(17), 6388; https://doi.org/10.3390/en15176388 - 01 Sep 2022

Cited by 2 | Viewed by 1660

Abstract

One of the most important and challenging research subjects in computer vision is visual object tracking. The information obtained from the first frame consists of limited and insufficient information to represent an object. If prior information about robust representation that can represent an object well is not sufficient, object tracking fails when not robustly responding to changes in features of the target object according to various factors, namely shape, illumination variation, and scene distortion. In this paper, a real-time single object tracking algorithm is proposed based on a Siamese network to solve this problem. For the object feature extraction, we designed a fully convolutional neural network that removes a fully connected layer and configured a convolution block consisting of a bottleneck structure that preserves the information in a previous layer. This network was designed as a Siamese network, while a regional proposal network was combined at the end of the network for object tracking. The ImageNet Large-Scale Visual Recognition Challenge 2017 dataset was used to train the network in the pre-training phase. Then, in the experimental phase, the object tracking benchmark dataset was used to quantitatively evaluate the network. The experimental results revealed that the proposed tracking algorithm produced more competitive results compared to other tracking algorithms. Full article

(This article belongs to the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization)

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