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To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Health, Well-Being and Sustainability".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 46254

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Special Issue Editors

Dr. Ali A. Alwan Al-juboori

E-Mail Website
Guest Editor

School of Theoretical and Applied Science, Ramapo College of New Jersey, Mahwah, NJ 07430, USA
Interests: artificial intelligence; machine learning; location-based social networks (lbsn); recommendation system; database systems

Dr. Yonis Gulzar

E-Mail Website
Guest Editor

Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Interests: deep learning; image processing; and computer vision; database systems; query processing

Special Issue Information

Dear Colleagues,

The adoption of artificial intelligence and blockchain in the field of healthcare and agriculture is progressing rapidly. Artificial intelligence has been already used in pre-clinical and clinical settings. As far as agriculture is concerned, artificial intelligence, especially deep learning and machine learning, has helped farmers in many ways, such as identifying plant diseases, crop yield prediction, crop and fruit classification. Most importantly, blockchain agriculture enables the traceability of information in the food supply chain to improve food safety. Blockchain's ability to store and manage data creates traceability, which is used to facilitate the development and use of innovations for intelligent farming and index-based agriculture insurance.

In this Special Issue, we aim to collate current research, focusing on applications of blockchain and artificial intelligence on various biomedical and agricultural data. In this Special Issue, original unpublished research articles and reviews are welcomed.

We look forward to receiving your contributions.

Dr. Ali A. Alwan Al-juboori
Dr. Yonis Gulzar
Guest Editors

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Keywords

sustainability
artificial intelligence
social sustainability
sustainable agriculture
precision agriculture
sustainable blockchain
sustainable healthcare
ai sustainability
sustainable development
image classification

Published Papers (13 papers)

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Research

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16 pages, 4431 KiB

Open AccessArticle

Maize Disease Classification System Design Based on Improved ConvNeXt

by Han Li, Mingyang Qi, Baoxia Du, Qi Li, Haozhang Gao, Jun Yu, Chunguang Bi, Helong Yu, Meijing Liang, Guanshi Ye and You Tang

Sustainability 2023, 15(20), 14858; https://doi.org/10.3390/su152014858 - 13 Oct 2023

Cited by 1 | Viewed by 1035

Abstract

Maize diseases have a great impact on agricultural productivity, making the classification of maize diseases a popular research area. Despite notable advancements in maize disease classification achieved via deep learning techniques, challenges such as low accuracy and identification difficulties still persist. To address these issues, this study introduced a convolutional neural network model named Sim-ConvNeXt, which incorporated a parameter-free SimAM attention module. The integration of this attention mechanism enhanced the ability of the downsample module to extract essential features of maize diseases, thereby improving classification accuracy. Moreover, transfer learning was employed to expedite model training and improve the classification performance. To evaluate the efficacy of the proposed model, a publicly accessible dataset with eight different types of maize diseases was utilized. Through the application of data augmentation techniques, including image resizing, hue, cropping, rotation, and edge padding, the dataset was expanded to comprise 17,670 images. Subsequently, a comparative analysis was conducted between the improved model and other models, wherein the approach demonstrated an accuracy rate of 95.2%. Notably, this performance represented a 1.2% enhancement over the ConvNeXt model and a 1.5% improvement over the advanced Swin Transformer model. Furthermore, the precision, recall, and F1 scores of the improved model demonstrated respective increases of 1.5% in each metric compared to the ConvNeXt model. Notably, using the Flask framework, a website for maize disease classification was developed, enabling accurate prediction of uploaded maize disease images. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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23 pages, 1485 KiB

Open AccessArticle

Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

by Poonam Dhiman, Amandeep Kaur, V. R. Balasaraswathi, Yonis Gulzar, Ali A. Alwan and Yasir Hamid

Sustainability 2023, 15(12), 9643; https://doi.org/10.3390/su15129643 - 15 Jun 2023

Cited by 31 | Viewed by 3664

Abstract

Different kinds of techniques are evaluated and analyzed for various classification models for the detection of diseases of citrus fruits. This paper aims to systematically review the papers that focus on the prediction, detection, and classification of citrus fruit diseases that have employed machine learning, deep learning, and statistical techniques. Additionally, this paper explores the present state of the art of the concept of image acquisition, digital image processing, feature extraction, and classification approaches, and each one is discussed separately. A total of 78 papers are selected after applying primary selection criteria, inclusion/exclusion criteria, and quality assessment criteria. We observe that the following are widely used in the selected studies: hyperspectral imaging systems for the image acquisition process, thresholding for image processing, support vector machine (SVM) models as machine learning (ML) models, convolutional neural network (CNN) architectures as deep learning models, principal component analysis (PCA) as a statistical model, and classification accuracy as evaluation parameters. Moreover, the color feature is the most popularly used feature for the RGB color space. From the review studies that performed comparative analyses, we find that the best techniques that outperformed other techniques in their respective categories are as follows: SVM among the ML methods, ANN among the neural network networks, CNN among the deep learning methods, and linear discriminant analysis (LDA) among the statistical techniques.This study concludes with meta-analysis, limitations, and future research directions. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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16 pages, 3373 KiB

Open AccessArticle

Static Evaluation of a Midimew Connected Torus Network for Next Generation Supercomputers

by Maryam Al-Shammari, Asrar Haque and M M Hafizur Rahman

Sustainability 2023, 15(8), 6766; https://doi.org/10.3390/su15086766 - 17 Apr 2023

Viewed by 1012

Abstract

Many artificially intelligent systems solve complex health- and agriculture-related problems that require great computational power. Such systems are used for tracking medical records, genome sequence analysis, image-based plant disease detection, food supply chain traceability, and photosynthesis simulation. Massively parallel computers (MPCs) are among those used to solve these computation-intensive problems. MPCs comprise a million nodes; connecting such a large number of nodes is a daunting task. Therefore, hierarchical interconnection networks (HINs) have been introduced to solve this problem. A midimew-connected torus network (MTN) is a HIN that has basic modules (BM) as torus networks that are connected hierarchically by midimew links. This paper presents the performance of MTNs in terms of static topological parameters and cost-effectiveness, as measured through simulations. An MTN was compared with other networks, including mesh, torus, TESH, TTN, MMN, and TFBN. The results showed that our MTN had a low diameter with a high bisection width and arc connectivity. In addition, our MTN had a high cost–performance trade-off factor (CPTF), a high cost-effective factor (CEF), low packing density, and moderate message-traffic density with marginally higher costs, as compared to other networks, due to wire complexity. However, our MTN provided better bandwidth with higher static fault tolerance. Therefore, MTNs are suggested for further evaluation of the effective implementation of MPCs. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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17 pages, 2463 KiB

Open AccessFeature PaperArticle

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

by Mohammed Al-Naeem, M M Hafizur Rahman, Anuradha Banerjee and Abu Sufian

Sustainability 2023, 15(8), 6421; https://doi.org/10.3390/su15086421 - 10 Apr 2023

Cited by 2 | Viewed by 1336

Abstract

Crop monitoring and smart spraying have become indispensable parts of precision agriculture where unmanned aerial vehicles (UAVs) play a lead role. In particular, in large agricultural fields, aerial monitoring is a sustainable solution provided it can be performed in an energy-efficient manner. The existing literature points out that the research on precision agriculture using UAVs is still very minimal. In this article, we propose a support vector machine (SVM)-based UAV location management technique where UAVs change position over various portions or regions of a large agricultural field so that crops are properly monitored in an energy-efficient manner. Whenever a processing request is generated from any sensor in a part of the field, the UAV investigates with an SVM to decide whether to move on to the center of that field based on various parameters or characteristics such as region-id, packet-id, time of day, waiting times of the packets, the average waiting time of others within a predefined time window, location of the UAV, residual energy of the UAV after processing the packet, and movement after processing the packet. We use 70% of our data for training and the other 30% for testing. In our simulation study, we use accuracy, precision, and recall to measure in both contexts to determine the efficiency of the model, and also the amount of energy preserved is computed corresponding to every move. We also compare our approach with current state-of-the-art energy-preserving UAV movement control techniques which are compatible with the present application scenario. The proposed technique produced 6.5%, 34.5%, and 61.5% better results in terms of percentage of successful detection (PSD), composite energy consumption (CEC), and average delay (ADL), respectively. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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23 pages, 2036 KiB

Open AccessArticle

Blockchain-Based Framework for Interoperable Electronic Health Records for an Improved Healthcare System

by Faheem Ahmad Reegu, Hafiza Abas, Yonis Gulzar, Qin Xin, Ali A. Alwan, Abdoh Jabbari, Rahul Ganpatrao Sonkamble and Rudzidatul Akmam Dziyauddin

Sustainability 2023, 15(8), 6337; https://doi.org/10.3390/su15086337 - 7 Apr 2023

Cited by 13 | Viewed by 7090

Abstract

The healthcare industry has been transitioning from paper-based medical records to electronic health records (EHRs) in most healthcare facilities. However, the current EHR frameworks face challenges in secure data storage, credibility, and management. Interoperability and user control of personal data are also significant concerns in the healthcare sector. Although block chain technology has emerged as a powerful solution that can offer the properties of immutability, security, and user control on stored records, its potential application in EHR frameworks is not yet fully understood. To address this gap in knowledge, this research aims to provide an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards such as HIPAA and HL7. The research method employed is a systematic literature review to explore the current state of the art in the field of EHRs, including blockchain-based implementations of EHRs. The study defines the interoperability issues in the existing blockchain-based EHR frameworks, reviews various national and international standards of EHR, and further defines the interoperability requirements based on these standards. The proposed framework can offer safer methods to interchange health information for the healthcare sector and can provide the properties of immutability, security, and user control on stored records without the need for centralized storage. The contributions of this work include enhancing the understanding of the potential application of blockchain technology in EHR frameworks and proposing an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards. Overall, this study has significant implications for the healthcare sector, as it can enhance the secure sharing and storage of electronic health data while ensuring the confidentiality, privacy, and integrity of medical records. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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18 pages, 3175 KiB

Open AccessArticle

Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing

by Poonam Dhiman, Amandeep Kaur, Yasir Hamid, Eatedal Alabdulkreem, Hela Elmannai and Nedal Ababneh

Sustainability 2023, 15(5), 4576; https://doi.org/10.3390/su15054576 - 3 Mar 2023

Cited by 13 | Viewed by 2826

Abstract

In recent decades, deep-learning dependent fruit disease detection and classification techniques have evinced outstanding results in technologically advanced horticulture investigation. Due to the comparatively limited image processing capabilities of edge computing devices, implementing deep learning methods in actual field scenarios is currently difficult. The use of intelligent machines in contemporary horticulture is being hampered by these restrictions, which are emerging as a new barrier. In this research, we present an efficient model for citrus fruit disease prediction. The proposed model utilizes the fusion of deep learning models CNN and LSTM with edge computing. The proposed model employs an enhanced feature-extraction mechanism, with a down-sampling approach, and then a feature-fusion subsystem to ensure significant recognition on edge computing devices with retaining citrus fruit disease detection accuracy. This research utilizes the online Kaggle and plan village dataset which contains 2950 citrus fruit images with disease categories black spots, cankers, scabs, Melanosis, and greening. The proposed model and existing model are tested with two features with pruning and without pruning and compared based on various performance measuring parameters, i.e., precision, recall, f-measure, and support. In the first phase experimental analysis is performed using Magnitude Based Pruning and in the second phase Magnitude Based Pruning with Post Quantization. The proposed CNN-LSTM model achieves an accuracy rate of 97.18% with Magnitude-Based Pruning and 98.25% with Magnitude-Based Pruning with Post Quantization, which is better as compared to the existing CNN method. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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14 pages, 1882 KiB

Open AccessArticle

Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique

by Yonis Gulzar

Sustainability 2023, 15(3), 1906; https://doi.org/10.3390/su15031906 - 19 Jan 2023

Cited by 88 | Viewed by 8309

Abstract

Due to the rapid emergence and evolution of AI applications, the utilization of smart imaging devices has increased significantly. Researchers have started using deep learning models, such as CNN, for image classification. Unlike the traditional models, which require a lot of features to perform well, CNN does not require any handcrafted features to perform well. It uses numerous filters, which extract required features from images automatically for classification. One of the issues in the horticulture industry is fruit classification, which requires an expert with a lot of experience. To overcome this issue an automated system is required which can classify different types of fruits without the need for any human effort. In this study, a dataset of a total of 26,149 images of 40 different types of fruits was used for experimentation. The training and test set were randomly recreated and divided into the ratio of 3:1. The experiment introduces a customized head of five different layers into MobileNetV2 architecture. The classification layer of the MobileNetV2 model is replaced by the customized head, which produced the modified version of MobileNetV2 called TL-MobileNetV2. In addition, transfer learning is used to retain the pre-trained model. TL-MobileNetV2 achieves an accuracy of 99%, which is 3% higher than MobileNetV2, and the equal error rate of TL-MobileNetV2 is just 1%. Compared to AlexNet, VGG16, InceptionV3, and ResNet, the accuracy is better by 8, 11, 6, and 10%, respectively. Furthermore, the TL-MobileNetV2 model obtained 99% precision, 99% for recall, and a 99% F1-score. It can be concluded that transfer learning plays a big part in achieving better results, and the dropout technique helps to reduce the overfitting in transfer learning. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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20 pages, 9266 KiB

Open AccessArticle

An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images

by Sonam Aggarwal, Sheifali Gupta, Deepali Gupta, Yonis Gulzar, Sapna Juneja, Ali A. Alwan and Ali Nauman

Sustainability 2023, 15(2), 1695; https://doi.org/10.3390/su15021695 - 16 Jan 2023

Cited by 43 | Viewed by 2824

Abstract

Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for protein subcellular localization have gained a lot of interest. The Human Protein Atlas (HPA) project is a macro-initiative that aims to map the human proteome utilizing antibody-based proteomics and related c. Millions of images have been tagged with single or multiple labels in the HPA database. However, fewer techniques for predicting the location of proteins have been devised, with the majority of them relying on automatic single-label classification. As a result, there is a need for an automatic and sustainable system capable of multi-label classification of the HPA database. Deep learning presents a potential option for automatic labeling of protein’s subcellular localization, given the vast image number generated by high-content microscopy and the fact that manual labeling is both time-consuming and error-prone. Hence, this research aims to use an ensemble technique for the improvement in the performance of existing state-of-art convolutional neural networks and pretrained models were applied; finally, a stacked ensemble-based deep learning model was presented, which delivers a more reliable and robust classifier. The F1-score, precision, and recall have been used for the evaluation of the proposed model’s efficiency. In addition, a comparison of existing deep learning approaches has been conducted with respect to the proposed method. The results show the proposed ensemble strategy performed exponentially well on the multi-label classification of Human Protein Atlas images, with recall, precision, and F1-score of 0.70, 0.72, and 0.71, respectively. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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19 pages, 4330 KiB

Open AccessArticle

Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach

by Normaisharah Mamat, Mohd Fauzi Othman, Rawad Abdulghafor, Ali A. Alwan and Yonis Gulzar

Sustainability 2023, 15(2), 901; https://doi.org/10.3390/su15020901 - 4 Jan 2023

Cited by 69 | Viewed by 4051

Abstract

An accurate image retrieval technique is required due to the rapidly increasing number of images. It is important to implement image annotation techniques that are fast, simple, and, most importantly, automatically annotate. Image annotation has recently received much attention due to the massive rise in image data volume. Focusing on the agriculture field, this study implements automatic image annotation, namely, a repetitive annotation task technique, to classify the ripeness of oil palm fruit and recognize a variety of fruits. This approach assists farmers to enhance the classification of fruit methods and increase their production. This study proposes simple and effective models using a deep learning approach with You Only Look Once (YOLO) versions. The models were developed through transfer learning where the dataset was trained with 100 images of oil fruit palm and 400 images of a variety of fruit in RGB images. Model performance and accuracy of automatically annotating the images with 3500 fruits were examined. The results show that the annotation technique successfully annotated a large number of images accurately. The mAP result achieved for oil palm fruit was 98.7% and the variety of fruit was 99.5%. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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13 pages, 2290 KiB

Open AccessArticle

Artificial Intelligence Model for Risk Management in Healthcare Institutions: Towards Sustainable Development

by Abdelaziz Darwiesh, A. H. El-Baz, Abedallah Zaid Abualkishik and Mohamed Elhoseny

Sustainability 2023, 15(1), 420; https://doi.org/10.3390/su15010420 - 27 Dec 2022

Cited by 3 | Viewed by 2880

Abstract

This paper proposes an artificial intelligence model to manage risks in healthcare institutions. This model uses a trendy data source, social media, and employs users’ interactions to identify and assess potential risks. It employs natural language processing techniques to analyze the tweets of users and produce vivid insights into the types of risk and their magnitude. In addition, some big data analysis techniques, such as classification, are utilized to reduce the dimensionality of the data and manage the data effectively. The produced insights will help healthcare managers to make the best decisions for their institutions and patients, which can lead to a more sustainable environment. In addition, we build a mathematical model for the proposed model, and some closed-form relations for risk analysis, identification and assessment are derived. Moreover, a case study on the CVS institute of healthcare in the USA, and our subsequent findings, indicate that a quartile of patients’ tweets refer to risks in CVS services, such as operational, financial and technological risks, and the magnitude of these risks vary between high risk (19%), medium risk (80.4%) and low risk (0.6%). Further, several performance measures and a complexity analysis are given to show the validity of the proposed model. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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Review

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28 pages, 1708 KiB

Open AccessReview

Email Security Issues, Tools, and Techniques Used in Investigation

by Esra Altulaihan, Abrar Alismail, M. M. Hafizur Rahman and Adamu A. Ibrahim

Sustainability 2023, 15(13), 10612; https://doi.org/10.3390/su151310612 - 5 Jul 2023

Cited by 3 | Viewed by 5598

Abstract

The email system is a globally distributed communication infrastructure service that involves multiple actors playing different roles to ensure end-to-end mail delivery. It is an indispensable method of communicating that is changing how people share data and information. As a result, it facilitates effective and efficient communication, especially in business, as well as convenience, accessibility, and replication. Today, email can send more than just text files; it can also send audio, video, photos, and other files with extensions. With email becoming ubiquitous in all aspects of our lives, enhancing its security, operating procedures, and forensic investigation has become essential. The purpose of this paper is to review some real email forensic incidents and the tools and techniques that have been proposed. A discussion of the major threats to email as well as techniques to mitigate them will also be provided. A comparison study was made of several techniques and analysis tools used in email forensics. In addition, this paper compares the available software tools for email forensics based on factors such as language interface, user interface, programming language, creation of image files, calculation of hash value, cost, and advantages. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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Other

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16 pages, 895 KiB

Open AccessSystematic Review

A Systematic Review on Water Fluoride Levels Causing Dental Fluorosis

by Muhammad Farooq Umer

Sustainability 2023, 15(16), 12227; https://doi.org/10.3390/su151612227 - 10 Aug 2023

Cited by 2 | Viewed by 2011

Abstract

Dental fluorosis is a long-existing public health issue resulting from inequitable access to potable water. Socially disadvantaged rural communities in fluoride-endemic areas, where a conventional irrigation system is absent and groundwater containing natural fluoride is the predominant source of drinking water, face a significant oral public health threat. This study aimed to determine the association between water fluoride levels and dental fluorosis. A systematic review aligned with PRISMA principles was conducted using the SPIDER search methodology and relevant keywords on many search engines, such as Google Scholar, PubMed, Elsevier, Sage, Web of Science, Cochrane, and Scopus. This review sought to ascertain the PICO model’s application as a search strategy tool. The reviewers gathered and assessed 1164 papers from January 2010 to January 2023. In total, 24 research papers from diverse databases were included. Using the Newcastle–Ottawa Scale, grades resulting from several data screens were evaluated. According to a previous systematic review, there may be publication bias in studies examining the association between fluoride in drinking water and dental fluorosis. The findings of this systematic review indicate that subpar fluoride is detrimental to human health. The author outlines legislative tools and technological advancements that might reduce fluoride levels. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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14 pages, 4426 KiB

Open AccessBrief Report

Sustainable Yield Prediction in Agricultural Areas Based on Fruit Counting Approach

by Amine Saddik, Rachid Latif, Abedallah Zaid Abualkishik, Abdelhafid El Ouardi and Mohamed Elhoseny

Sustainability 2023, 15(3), 2707; https://doi.org/10.3390/su15032707 - 2 Feb 2023

Cited by 5 | Viewed by 1799

Abstract

A sustainable yield prediction in agricultural fields is a very critical task that aims to help farmers have an idea about agricultural operations. Generally, we can find a variety of applications proposed for this purpose that include fruit counting. These applications are based on Artificial Intelligence, especially Deep Learning (DL) and Machine Learning (ML) approaches. These approaches give reliable counting accuracy, but the problem is the use of a large database to achieve the desired accuracy. That makes these approaches limited. For this reason, in this work, we propose a low-complexity algorithm that aims to count green and red apples based on our real dataset collected in the Moroccan region, Fes-Meknes. This algorithm allowed us to further increase sustainability in agricultural fields based on yield prediction. The proposed approach was based on HSV conversion and the Hough transform for fruit counting. The algorithm was divided into three blocks based on image acquisition and filtering for the first block. The second block is the conversion to HSV and the detection of fruits. Finally, the counting operation for the third block. Subsequently, we proposed an implementation based on the low-cost Raspberry system and a desktop. The results show that we can reach 15 fps in the case of the Raspberry architecture and 40 fps based on the desktop. Our proposed system can inform agricultural policy by providing accurate and timely information on crop production, which can be used to guide decisions on food supply and distribution. Full article

(This article belongs to the Special Issue To the Future: Adoption of Artificial Intelligence and Blockchain in Agriculture and Healthcare from a Sustainability Perspective)

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