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Latest Applications of Computer Vision and Machine Learning Techniques for Smart Sustainability

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

Deadline for manuscript submissions: closed (24 October 2023) | Viewed by 9656

Special Issue Editors

Prof. Dr. Ahmad Hassanat

E-Mail Website
Guest Editor
Computer Science Department, Mutah University, Karak 61711, Jordan
Interests: artificial intelligence; machine learning; image processing/analysis; computer vision
Dr. Sami Mnasri

E-Mail Website
Guest Editor
1. Department of Computer Science, Applied College, University of Tabuk, Tabuk 71491, Saudi Arabia
2. CNRS-IRIT (RMESS), University of Toulouse, 31000 Toulouse, France
Interests: artificial intelligence; machine learning; optimization; smart cities; smart networks
Dr. Ahmad S. Tarawneh

E-Mail Website
Guest Editor
Department of Data Science and Artificial Intelligence, University of Petra, Amman 11196, Jordan
Interests: machine leaning; deep learning; big data; computer vision

Special Issue Information

Dear Colleagues,

In order to increase the efficiency and effectiveness of sustainable practices, smart sustainability refers to the integration of smart technology with sustainability concepts. The scope of smart sustainability is quite broad and has a wide range of applications related to a number of research areas such as environmental issues, energy, transportation, waste management, water conservation, agricultural and urban planning, among others. The application of computer vision and machine learning techniques (CVML) in sustainability has the potential to revolutionize various sectors and encourage more sustainable practices. The most recent CVML developments can be applied to deliver smart solutions to various sustainability issues related to environment, agriculture, energy, transportation, waste management, urban planning, among others.

In this Special Issue, original research articles and reviews in CVML for smart sustainability are welcomed. Research areas may include (but not limited to) the following:

  1. Environmental issues including, but not limited to, promoting sustainable food systems, limiting water pollution, and enhancing air quality.
  2. Agricultural issues including, but not limited to, crop health analysis, pest and disease detection, and irrigation systems.
  3. Energy issues including, but not limited to, reducing energy consumption, detecting energy losses, and predicting its demand.
  4. Transportation issues including, but not limited to, traffic management, improving services, reducing emissions, and vehicle management.
  5. Waste management, such as improving waste collection, detecting and sorting recyclable materials, and reducing waste contamination, etc.
  6. Urban planning such as analyzing urban landscapes, forecasting urban expansion, issues regarding smart cities, deployment, optimizing land usage, etc. 
  7. Any related sustainability issues.

We look forward to receiving your contributions. 

Prof. Dr. Ahmad Hassanat
Dr. Sami Mnasri
Dr. Ahmad S. Tarawneh
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

  • machine learning
  • compute vision
  • smart sustainability
  • smart transportation
  • smart energy
  • smart food
  • smart cities
  • smart farming

Published Papers (4 papers)

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Research

14 pages, 2673 KiB  
Article
Semantic and Instance Segmentation in Coastal Urban Spatial Perception: A Multi-Task Learning Framework with an Attention Mechanism
by Hanwen Zhang, Hongyan Liu and Chulsoo Kim
Sustainability 2024, 16(2), 833; https://doi.org/10.3390/su16020833 - 18 Jan 2024
Cited by 9 | Viewed by 841
Abstract
With the continuous acceleration of urbanization, urban planning and design require more in-depth research and development. Street view images can express rich urban features and guide residents’ emotions toward a city, thereby providing the most intuitive reflection of their perception of the city’s [...] Read more.
With the continuous acceleration of urbanization, urban planning and design require more in-depth research and development. Street view images can express rich urban features and guide residents’ emotions toward a city, thereby providing the most intuitive reflection of their perception of the city’s spatial quality. However, current researchers mainly conduct research on urban spatial quality through subjective experiential judgment, which includes problems such as a high cost and a low judgment accuracy. In response to these problems, this study proposes a multi-task learning urban spatial attribute perception model that integrates an attention mechanism. Via this model, the existing attributes of urban street scenes are analyzed. Then, the model is improved by introducing semantic segmentation and instance segmentation to identify and match the qualities of the urban space. The experimental results show that the multi-task learning urban spatial attribute perception model with an integrated attention mechanism has prediction accuracies of 79.54%, 78.62%, 79.68%, 77.42%, 78.45%, and 76.98% for the urban spatial attributes of beauty, boredom, depression, liveliness, safety, and richness, respectively. The accuracy of the multi-task learning urban spatial scene feature image segmentation model with an integrated attention mechanism is 95.4, 94.8, 96.2, 92.1, and 96.7 for roads, walls, sky, vehicles, and buildings, respectively. The multi-task learning urban spatial scene feature image segmentation model with an integrated attention mechanism has a higher recognition accuracy for urban spatial buildings than other models. These research results indicate the model’s effectiveness in matching urban spatial quality with public perception. Full article
(This article belongs to the Special Issue Latest Applications of Computer Vision and Machine Learning Techniques for Smart Sustainability)
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19 pages, 6676 KiB  
Article
Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences
by Naif Al Mudawi, Asifa Mehmood Qureshi, Maha Abdelhaq, Abdullah Alshahrani, Abdulwahab Alazeb, Mohammed Alonazi and Asaad Algarni
Sustainability 2023, 15(19), 14597; https://doi.org/10.3390/su151914597 - 8 Oct 2023
Cited by 4 | Viewed by 5394
Abstract
Vehicle detection and classification are the most significant and challenging activities of an intelligent traffic monitoring system. Traditional methods are highly computationally expensive and also impose restrictions when the mode of data collection changes. This research proposes a new approach for vehicle detection [...] Read more.
Vehicle detection and classification are the most significant and challenging activities of an intelligent traffic monitoring system. Traditional methods are highly computationally expensive and also impose restrictions when the mode of data collection changes. This research proposes a new approach for vehicle detection and classification over aerial image sequences. The proposed model consists of five stages. All of the images are preprocessed in the first stage to reduce noise and raise the brightness level. The foreground items are then extracted from these images using segmentation. The segmented images are then passed onto the YOLOv8 algorithm to detect and locate vehicles in each image. The feature extraction phase is then applied to the detected vehicles. The extracted feature involves Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and KAZE features. For classification, we used the Deep Belief Network (DBN) classifier. Based on classification, the experimental results across the three datasets produced better outcomes; the proposed model attained an accuracy of 95.6% over Vehicle Detection in Aerial Imagery (VEDAI) and 94.6% over Vehicle Aerial Imagery from a Drone (VAID) dataset, respectively. To compare our model with the other standard techniques, we have also drawn a comparative analysis with the latest techniques in the research. Full article
(This article belongs to the Special Issue Latest Applications of Computer Vision and Machine Learning Techniques for Smart Sustainability)
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16 pages, 33690 KiB  
Article
MSCF: Multi-Scale Canny Filter to Recognize Cells in Microscopic Images
by Almoutaz Mbaidin, Eva Cernadas, Zakaria A. Al-Tarawneh, Manuel Fernández-Delgado, Rosario Domínguez-Petit, Sonia Rábade-Uberos and Ahmad Hassanat
Sustainability 2023, 15(18), 13693; https://doi.org/10.3390/su151813693 - 13 Sep 2023
Cited by 2 | Viewed by 1070
Abstract
Fish fecundity is one of the most relevant parameters for the estimation of the reproductive potential of fish stocks, used to assess the stock status to guarantee sustainable fisheries management. Fecundity is the number of matured eggs that each female fish can spawn [...] Read more.
Fish fecundity is one of the most relevant parameters for the estimation of the reproductive potential of fish stocks, used to assess the stock status to guarantee sustainable fisheries management. Fecundity is the number of matured eggs that each female fish can spawn each year. The stereological method is the most accurate technique to estimate fecundity using histological images of fish ovaries, in which matured oocytes must be measured and counted. A new segmentation technique, named the multi-scale Canny filter (MSCF), is proposed to recognize the boundaries of cells (oocytes), based on the Canny edge detector. Our results show the superior performance of MSCF on five fish species compared to five other state-of-the-art segmentation methods. It provides the highest F1 score in four out of five fish species, with values between 70% and 80%, and the highest percentage of correctly recognized cells, between 52% and 64%. This type of research aids in the promotion of sustainable fisheries management and conservation efforts, decreases research’s environmental impact and gives important insights into the health of fish populations and marine ecosystems. Full article
(This article belongs to the Special Issue Latest Applications of Computer Vision and Machine Learning Techniques for Smart Sustainability)
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19 pages, 22837 KiB  
Article
Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants
by Eman S. Alamri, Ghada A. Altarawneh, Hala M. Bayomy and Ahmad B. Hassanat
Sustainability 2023, 15(15), 11561; https://doi.org/10.3390/su151511561 - 26 Jul 2023
Cited by 2 | Viewed by 1561
Abstract
This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited [...] Read more.
This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited real-world data availability and the high costs involved with laboratory testing. The Monte Carlo approach is used to generate new samples for each type of Arabic coffee using the mean values and standard deviations of publicly available data. Using these simulated data, multiple machine-learning algorithms are used to classify Arabic coffee, while also investigating the importance of features in identifying the key chemical components. The findings emphasize the importance of color information in accurately recognizing Arabic coffee types. However, depending purely on antioxidant information results in poor classification accuracy due to increased data complexity and classifier variability. The chemical composition information, on the other hand, has exceptional discriminatory power, allowing faultless classification on its own. Notably, particular characteristics like crude protein and crude fiber show high relationships and play an important role in coffee type classification. Based on these findings, it is suggested that a mobile application be developed that uses image recognition to examine coffee color while also providing chemical composition information. End users, especially consumers, would be able to make informed judgments regarding their coffee preferences. Full article
(This article belongs to the Special Issue Latest Applications of Computer Vision and Machine Learning Techniques for Smart Sustainability)
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