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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 1129

Special Issue Editors


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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

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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

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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Published Papers (1 paper)

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Research

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 1 | Viewed by 925
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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