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Agricultural Engineering for Sustainable Development
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Agricultural Engineering for Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 3455

Special Issue Editors

Dr. Nikolay Kiktev

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Guest Editor
Department of Automation and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, 03041 Kyiv, Ukraine
Interests: automated information and control systems for managing technological processes and production in agro-industrial complexes; data analytics; robotics; IoT; machine learning
Dr. Taras Lendiel

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Guest Editor
Department of Automation and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, 03041 Kyiv, Ukraine
Interests: agricultural engineering; control system engineering; computer communications (networks); artificial neural networks
Dr. Oleksii Opryshko

E-Mail Website
Guest Editor
Department of Automation and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, 03041 Kyiv, Ukraine
Interests: industrial design; agronomy; bioengineering; control system engineering

Special Issue Information

Dear Colleagues,

With the rapid growth of the world's population, there is a need to increase the quantity and quality of agricultural products; therefore, adopting a sustainable approach to research in this area based on advanced engineering solutions and information technology is crucial. At the moment, there are several sustainability issues throughout the entire agricultural production chain and this has necessitated the development and implementation of innovative technological solutions such as autonomous vehicles, navigation, forecasting models, artificial intelligence and machine learning, the Internet of Things and robotics, digitalization, data analytics, etc. It is very important that these innovations bring economic benefits and meet the requirements of consumers, producers, and agriculture-adjacent industries. Today, there are many publications in the field of agricultural engineering, but they are mostly local in nature, dealing with individual problems. We propose to apply an integrated systems approach to innovative solutions in this area aimed at the sustainable development of agriculture. With this in mind, this Special Issue welcomes submissions of original research articles, short communications and reviews focused on identifying the current challenges of and future opportunities/applications in the global agricultural sector.

The aim of this Special Issue is to create a forum for experts, professionals, and readers interested in topics related to agricultural engineering, energy, automation, robotics, information technology, artificial intelligence in agriculture, and its sustainable development.

This Special Issue welcomes original research articles and reviews and research areas may include, but are not limited to:

  • Agricultural machinery, vehicle control and navigation, and route optimization;
  • Information technology, artificial intelligence, information security for planning, and the forecasting and management of agricultural production;
  • The use of machine learning and neural networks for pattern recognition in agronomy and animal husbandry;
  • The automation and robotization of agriculture, sensors, IoT, unmanned vehicles, and UAVs;
  • Energy efficient agriculture and renewable energy;
  • Agricultural economics and management for sustainable agricultural development.

We look forward to your contributions.

Dr. Nikolay Kiktev
Dr. Taras Lendiel
Dr. Oleksii Opryshko
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. Sustainability 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 agriculture
  • agricultural machinery navigation
  • sustainable agriculture
  • artificial intelligence
  • automation and robotics
  • controlled agricultural environments
  • planning, management, and forecasting for agricultural production

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

Published Papers (2 papers)

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Research

23 pages, 12551 KiB  
Article
Evaluation of Promising Areas for Biogas Production by Indirect Assessment of Raw Materials Using Satellite Monitoring
by Oleksiy Opryshko, Nikolay Kiktev, Sergey Shvorov, Fedir Hluhan, Roman Polishchuk, Maksym Murakhovskiy, Taras Hutsol, Szymon Glowacki, Tomasz Nurek and Mariusz Sojak
Sustainability 2025, 17(5), 2098; https://doi.org/10.3390/su17052098 - 28 Feb 2025
Viewed by 784
Abstract
An important issue in the sustainable development of agricultural engineering today is the use of biogas plants for the production of electricity and heat from the organic waste of agricultural products and other low-quality products, which also contributes to the improvement of environmental [...] Read more.
An important issue in the sustainable development of agricultural engineering today is the use of biogas plants for the production of electricity and heat from the organic waste of agricultural products and other low-quality products, which also contributes to the improvement of environmental safety. Traditional methods for assessing the apparent severity of the Roslynnytsia campaign based on statistics from the dominions proved to be ineffective. A hypothesis was proposed regarding the possibility of estimating the apparent biomass by averaging the indicators of depletion and assessing the CH4 and CO emissions based on satellite monitoring data. The aim of this work is to create a methodology for preparing a raw material base in united territorial communities to provide them with electrical and thermal energy using biogas plants. The achievement of this goal was based on solving the following tasks: monitoring biomethane emissions in the atmosphere as a result of rotting organic waste, and monitoring carbon monoxide emissions as a result of burning agricultural waste. Experimental studies were conducted using earth satellites on sites with geometric centers in the village of Gaishin in the Pereyaslav united territorial community, the city of Ovruch in the Zhytomyr region, the Oleshkovsky Sands National Park in the Kherson region (Ukraine), and the city of Jüterbog, which is located in the state of Brandenburg and is part of the Teltow-Fläming district (Germany). The most significant results of this research involve the methodology for the preparation of the raw material base in the united territorial communities for the production of biogas, based on indirect measurements of methane and carbon dioxide emissions using the process of remote sensing. Based on the use of the proposed scientific and methodological apparatus, it was found that the location of the territory with the center in the village of Gaishin has better prospects for collecting plant raw materials for biogas production than the location of the territorial district with the center in the city of Ovruch, the emissions in which are significantly lower. From March 2020–August 2023, a higher CO concentration was recorded on average by 0.0009 mol/m2, which is explained precisely by crop growing practices. In addition, as a result of the conducted studies, for the considered emissions of methane and carbon monoxide for monitoring promising raw materials, carbon monoxide has the best prospects, since methane emissions can also be caused by anthropogenic factors. Thus, in the desert (Oleshkivskie Pisky), large methane emissions were recorded throughout the year which could not be explained by crop growing practices or the livestock industry. Full article
(This article belongs to the Special Issue Agricultural Engineering for Sustainable Development)
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25 pages, 10652 KiB  
Article
Enhancing Sustainable Automated Fruit Sorting: Hyperspectral Analysis and Machine Learning Algorithms
by Dmitry O. Khort, Alexey Kutyrev, Igor Smirnov, Nikita Andriyanov, Rostislav Filippov, Andrey Chilikin, Maxim E. Astashev, Elena A. Molkova, Ruslan M. Sarimov, Tatyana A. Matveeva and Sergey V. Gudkov
Sustainability 2024, 16(22), 10084; https://doi.org/10.3390/su162210084 - 19 Nov 2024
Cited by 5 | Viewed by 2096
Abstract
Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning [...] Read more.
Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning to improve product quality and reduce losses. The employed hyperspectral technologies and machine learning algorithms enable the rapid and accurate detection of defects on the surface of fruits, enhancing product quality and reducing the number of rejects, thereby contributing to the sustainability of agriculture. This study seeks to advance commercial fruit quality control by comparing hyperspectral image classification algorithms to detect apple lesions caused by pathogens, including sunburn, scab, and rot, on three apple varieties: Honeycrisp, Gala, and Jonagold. The lesions were confirmed independently using expert judgment, real-time PCR, and 3D fluorimetry, providing a high accuracy of ground truth data and allowing conclusions to be drawn on ways to improve the sustainability and safety of the agrocenosis in which the fruits are grown. Hyperspectral imaging combined with mathematical analysis revealed that Venturia inaequalis is the main pathogen responsible for scab, while Botrytis cinerea and Penicillium expansum are the main causes of rot. This comparative study is important because it provides a detailed analysis of the performance of both supervised and unsupervised classification methods for hyperspectral imagery, which is essential for the development of reliable automated grading systems. Support Vector Machines (SVM) proved to be the most accurate, with the highest average adjusted Rand Index (ARI) scores for sunscald (0.789), scab (0.818), and rot (0.854), making it the preferred approach for classifying apple lesions during grading. K-Means performed well for scab (0.786) and rot (0.84) classes, but showed limitations with lower metrics for other lesion types. A design and technological scheme of an optical system for identifying micro- and macro-damage to fruit tissues is proposed, and the dependence of the percentage of apple damage on the rotation frequency of the sorting line rollers is obtained. The optimal values for the rotation frequency of the rollers, at which the damage to apples is less than 5%, are up to 6 Hz. The results of this study confirm the high potential of hyperspectral data for the non-invasive recognition and classification of apple diseases in automated sorting systems with an accuracy comparable to that of human experts. These results provide valuable insights into the optimization of machine learning algorithms for agricultural applications, contributing to the development of more efficient and accurate fruit quality control systems, improved production sustainability, and the long-term storage of fruits. Full article
(This article belongs to the Special Issue Agricultural Engineering for Sustainable Development)
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