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Sustainability
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Empowering Artificial Intelligence to Achieve Sustainable Development Goals
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Empowering Artificial Intelligence to Achieve Sustainable Development Goals

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4369

Special Issue Editors

Prof. Dr. Olexander Barmak

E-Mail Website
Guest Editor
Department of Computer Science, Khmelnytskyi National University, 29016 Khmelnytskyi, Ukraine
Interests: artificial intelligence (AI); AI transparency; sustainable practices in AI-driven information systems; robotics; non-verbal communication; analysis of textual information; pattern recognition; data communication and visualization; visual analysis; facial emotion recognition
Prof. Dr. Iurii Krak

E-Mail Website
Guest Editor
Department of Theoretical Cybernetics, Taras Shevchenko National University of Kyiv, 03680 Kyiv, Ukraine
Interests: intelligent decision-making systems; robotics; human-computer interface; big data analysis; responsible artificial intelligence deployment; data and image processing; information technologies for applications in medicine
Prof. Dr. Eduard Manziuk

E-Mail Website
Guest Editor
Department of Computer Science, Khmelnytskyi National University, 29016 Khmelnytskyi, Ukraine
Interests: artificial intelligence (AI); transparency in AI; trusty AI; fair and sustainable AI practices; responsible AI in climate action and energy efficiency; big data and machine learning; natural language processing; data communication and visualization; visual analytics
Dr. Pavlo Radiuk

E-Mail Website
Guest Editor
Department of Computer Science, Khmelnytskyi National University, 29016 Khmelnytskyi, Ukraine
Interests: artificial intelligence (AI); computer vision and pattern recognition; deep learning; AI in healthcare; human-controlled decision systems; trustworthy and explainable AI

Special Issue Information

Dear Colleagues,

As we stand at the crossroads of technology and global progress, artificial intelligence (AI) has emerged as a powerful catalyst for sustainable development. This Special Issue, "Empowering Artificial Intelligence to Achieve Sustainable Development Goals," aims to explore how AI can be effectively applied to meet the United Nations' Sustainable Development Goals (SDGs) outlined in the 2030 Agenda.

We invite scholars, practitioners, and innovators to contribute original research, insightful reviews, and case studies that highlight the transformative potential of AI in promoting sustainability. Our focus is on leveraging AI technologies to address pressing global challenges such as climate change, poverty, inequality, healthcare, and education. By showcasing practical applications and innovative solutions, we aim to demonstrate how AI can accelerate progress toward environmental, cultural, economic, and social well-being.

This Special Issue seeks to foster a multidisciplinary dialog that bridges the gap between AI advancements and sustainable outcomes. It will contribute to sustainability by providing an open platform for discussing how AI can be utilized across various sectors to achieve the SDGs, encouraging collaboration and knowledge sharing.

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

* AI applications in environmental monitoring and conservation;
* Machine learning for renewable energy optimization;
* AI-driven solutions for poverty alleviation and economic development;
* AI in education for promoting inclusive and equitable learning;
* Smart cities and AI for sustainable urban development;
* AI in disaster prediction and management;
* Policy and governance of ethical AI for sustainability;
* Transparency and fairness considerations in AI for economic growth and decent work;
* Case studies of transparent and fair AI implementation in achieving SDGs.

We look forward to your valuable contributions that will help shape a sustainable and prosperous future for all.

Prof. Dr. Olexander Barmak
Prof. Dr. Iurii Krak
Prof. Dr. Eduard Manziuk
Dr. Pavlo Radiuk
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

  • artificial intelligence (AI)
  • big data and machine learning
  • sustainable practices in AI-driven information systems
  • intelligent decision-making systems
  • transparent AI systems
  • explainable AI
  • interpretable AI
  • AI for social equity and inclusion
  • data communication and visualization

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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

19 pages, 10948 KiB  
Article
Detecting Plant Diseases Using Machine Learning Models
by Nazar Kohut, Oleh Basystiuk, Nataliya Shakhovska and Nataliia Melnykova
Sustainability 2025, 17(1), 132; https://doi.org/10.3390/su17010132 - 27 Dec 2024
Cited by 1 | Viewed by 1658
Abstract
Sustainable agriculture is pivotal to global food security and economic stability, with plant disease detection being a key challenge to ensuring healthy crop production. The early and accurate identification of plant diseases can significantly enhance agricultural practices, minimize crop losses, and reduce the [...] Read more.
Sustainable agriculture is pivotal to global food security and economic stability, with plant disease detection being a key challenge to ensuring healthy crop production. The early and accurate identification of plant diseases can significantly enhance agricultural practices, minimize crop losses, and reduce the environmental impacts. This paper presents an innovative approach to sustainable development by leveraging machine learning models to detect plant diseases, focusing on tomato crops—a vital and globally significant agricultural product. Advanced object detection models including YOLOv8 (minor and nano variants), Roboflow 3.0 (Fast), EfficientDetV2 (with EfficientNetB0 backbone), and Faster R-CNN (with ResNet50 backbone) were evaluated for their precision, efficiency, and suitability for mobile and field applications. YOLOv8 nano emerged as the optimal choice, offering a mean average precision (MAP) of 98.6% with minimal computational requirements, facilitating its integration into mobile applications for real-time support to farmers. This research underscores the potential of machine learning in advancing sustainable agriculture and highlights future opportunities to integrate these models with drone technology, Internet of Things (IoT)-based irrigation, and disease management systems. Expanding datasets and exploring alternative models could enhance this technology’s efficacy and adaptability to diverse agricultural contexts. Full article
(This article belongs to the Special Issue Empowering Artificial Intelligence to Achieve Sustainable Development Goals)
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15 pages, 2935 KiB  
Article
Evaluation of Pothole Detection Performance Using Deep Learning Models Under Low-Light Conditions
by Yuliia Zanevych, Vasyl Yovbak, Oleh Basystiuk, Nataliya Shakhovska, Solomiia Fedushko and Sotirios Argyroudis
Sustainability 2024, 16(24), 10964; https://doi.org/10.3390/su162410964 - 13 Dec 2024
Cited by 1 | Viewed by 2197
Abstract
In our interconnected society, prioritizing the resilience and sustainability of road infrastructure has never been more critical, especially in light of growing environmental and climatic challenges. By harnessing data from various sources, we can proactively enhance our ability to detect road damage. This [...] Read more.
In our interconnected society, prioritizing the resilience and sustainability of road infrastructure has never been more critical, especially in light of growing environmental and climatic challenges. By harnessing data from various sources, we can proactively enhance our ability to detect road damage. This approach will enable us to make well-informed decisions for timely maintenance and implement effective mitigation strategies, ultimately leading to safer and more durable road systems. This paper presents a new method for detecting road potholes during low-light conditions, particularly at night when influenced by street and traffic lighting. We examined and assessed various advanced machine learning and computer vision models, placing a strong emphasis on deep learning algorithms such as YOLO, as well as the combination of Grad-CAM++ with feature pyramid networks for feature extraction. Our approach utilized innovative data augmentation techniques, which enhanced the diversity and robustness of the training dataset, ultimately leading to significant improvements in model performance. The study results reveal that the proposed YOLOv11+FPN+Grad-CAM model achieved a mean average precision (mAP) score of 0.72 for the 50–95 IoU thresholds, outperforming other tested models, including YOLOv8 Medium with a score of 0.611. The proposed model also demonstrated notable improvements in key metrics, with mAP50 and mAP75 values of 0.88 and 0.791, reflecting enhancements of 1.5% and 5.7%, respectively, compared to YOLOv11. These results highlight the model’s superior performance in detecting potholes under low-light conditions. By leveraging a specialized dataset for nighttime scenarios, the approach offers significant advancements in hazard detection, paving the way for more effective and timely driver alerts and ultimately contributing to improved road safety. This paper makes several key contributions, including implementing advanced data augmentation methods and a thorough comparative analysis of various YOLO-based models. Future plans involve developing a real-time driver warning application, introducing enhanced evaluation metrics, and demonstrating the model’s adaptability in diverse environmental conditions, such as snow and rain. The contributions significantly advance the field of road maintenance and safety by offering a robust and scalable solution for pothole detection, particularly in developing countries. Full article
(This article belongs to the Special Issue Empowering Artificial Intelligence to Achieve Sustainable Development Goals)
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