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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

Multimedia Tools and Applications 2025年第7期84卷 3723-3743页

作者： Shah, Kanish Sushra, Rajat Shah, Manan Shah, Dhairya Shah, Haard Raval, Megh Prajapati, Mitul Software Engineer Walmart SunnyvaleCA United States Atmiya Vidya Mandir Gujarat Surat India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Robotics ECE Concentration Northeastern University BostonMA United States Department of Computer Science Concentration in AI Hofstra University HempsteadNY United States Department of Chemical Engineering S. S Agrawal Institute of Engineering & Technology Gujarat Navsari India R &D Engineer Span Inspection Systems Pvt. Ltd Gujarat Gandhinagar India

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Secure kNN For Distributed Cloud Environment Using Fully Homomorphic Encryption

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IEEE Transactions on Cloud Computing 2025年

作者： Fukuchi, Yuuya Hashimoto, Sota Sakai, Kazuya Fukumoto, Satoshi Sun, Min-Te Ku, Wei-Shinn Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo Japan Tokyo Metropolitan University Department of Computer Science Tokyo Japan National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan Auburn University Department of Computer Science and Software Engineering AuburnAL United States

Privacy-preserving k-nearest neighbor (PPkNN) classification for multiple clouds enables categorizing queried data into a class in keeping with data privacy, where the database and key servers jointly perform cryptographic operations. The existing solutions, unfortunately, take a long time and incur a large amount of traffic between the database and key servers. Therefore, in this paper, we propose a fast and secure kNN classification protocol, namely FSkNN, over distributed databases deployed in multiple clouds under the semi-honest model. Particularly, we focus on optimizing the network-related operations during kNN classification. That is, the proposed cryptographic protocol reduces the number of interactions between the servers by using a fully homomorphic encryption scheme and eliminates unnecessary traffic by applying mathematical techniques. In addition, the indistinguishability-based security of FSkNN is proven. We implemented FSkNN with C++ and the testbed experiments demonstrate that the proposed scheme significantly facilitates the query response time and reduces the communication cost. © 2013 IEEE.

关键词： Differential privacy

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Elastic Spectrum Sensing: An Adaptive Sensing Method for Non-Terrestrial Communication Under Highly Dynamic Channels

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3868-3881页

作者： Xu, Tianheng Xu, Yinjun Zhou, Ting Zhang, Haijun Hu, Honglin Leung, Victor C. M. Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Shanghai Frontier Innovation Research Institute Shanghai201100 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Shanghai University School of Microelectronics Shanghai200444 China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

With the rapid development of space technology, the role of satellite communications has become progressively significant. Non-terrestrial communication is deemed a critical scenario in the sixth generation (6G) communication systems, which showcases seamless connectivity, minimal geographic constraints and substantial communication capacity. Simultaneously, satellites and terminals spring up, spatial density increases, which further emphasizes the scarcity of spectrum resources. Consequently, to improve spectrum utilization for non-terrestrial communication is a significant concern. Spectrum sensing, which allows dynamic resource reuse, plays an important role in 6G. However, high mobility in non-terrestrial scenarios poses great challenges, such as fast time-varying channels, Doppler effect, etc., which seriously affect sensing accuracy and cannot well support optimal spectrum utilization. Motivated by such circumstances, this paper proposes an elastic sensing method for the downlink non-terrestrial communication scenario. Firstly, we design the system architecture and sensing workflow. To overcome the negative effects raised by high mobility, we propose the elastic sensing criterion and multi-area dividing scheme for the sensing zone. Thresholds affected by the elastic sensing are derived for different areas. Finally, the numerical results show that from -10 dB to -5 dB, the proposed method can improve the total performance by an average of 28.3% while stabilizing the false alarm probability around 0.1 typical level and demonstrating higher constancy compared with traditional technologies from −10 dB to −5 dB. © 2002-2012 IEEE.

关键词： Satellite communication systems

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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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Analyzing Digital Image Anti-Forensics Techniques and Countermeasures 27

Analyzing Digital Image Anti-Forensics Techniques and Counte...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Agarwal, Saurabh Kumar, Rajeev Devi, Abha Jung, Ki-Hyun Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of Blockchain Technology Research Lab Department of Computer Science and Engineering Delhi Technological University New Delhi110042 India Department of Software Convergence Andong National University Gyeongbuk36729 Korea Republic of College Uttar Pradesh Sikandrabad India

ISBN: (纸本)9791188428137

With the increasing use of digital media, digital forensics has become a crucial method for ascertaining authenticity and detecting manipulations in images. However, intentional anti-forensic manipulations to hide forensic clues have made verification more difficult. The efforts to develop countermeasures against anti-forensics have contributed to the evolution toward robust techniques in the field of anti-forensics;hence, the development of anti-forensic methods and their respective countermeasures will remain continuous. This work provides a scientometric analysis, a powerful tool for the study of the growth of research output in accordance with time and citation, author, organizational, and keyword co-occurrence analysis. It enhances state-of-the-art analysis by underlining highly influential authors, organizations, and works and stipulates knowledge gaps. This would consequently provide a platform for building a community where insights and inclusiveness among researchers, industry practitioners, and policymakers would all come together to guide their strategies and decisions for innovation in this dynamic field. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Fake detection

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A novel hybrid ConvNeXt-based approach for enhanced skin lesion classification

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Expert Systems with Applications 2025年 283卷

作者： Aruk, Ibrahim Pacal, Ishak Toprak, Ahmet Nusret Department of Computer Engineering Faculty of Engineering Erciyes University Kayseri38000 Turkey Department of Software Engineering Faculty of Engineering Architecture and Design Kahramanmaras Istiklal University Kahramanmaras46050 Turkey Department of Computer Engineering Faculty of Engineering Igdir University Igdir76000 Turkey Department of Electronics and Information Technologies Faculty of Architecture and Engineering Nakhchivan State University NakhchivanAZ 7012 Azerbaijan

Skin cancer is a serious health problem that, if not diagnosed correctly at an early stage, leads to disease progression, increased risk of metastasis, and decreased quality of life. Therefore, it is critical to develop accurate and effective rapid diagnosis methods to assist physicians and enable autonomous diagnosis. While deep learning-based methods generally rely on convolutional neural networks (CNNs) or vision transformers (ViTs), hybrid approaches remain limited. In this work, we propose a novel proposed model that combines the strengths of both CNN and ViT architectures. The model utilizes the CNN approach to extract local patterns and detailed textural features, while using ViT structures to capture long-range relationships between skin cancer regions. Initially, ConvNeXt blocks extract highly detailed local features such as texture, edges, and color transitions. Transformer blocks then model the contextual relationships and global structure of these features, effectively addressing data imbalance and improving the overall classification performance. For a fair evaluation, the model is tested on two widely recognized skin cancer datasets, HAM10000 and ISIC 2019, and compared with 10 CNN and 10 ViT-based models trained under the same conditions. The proposed model achieves 94.30% accuracy and 91.11% F1-score on HAM10000, and 92.50% accuracy and 90.38% F1-score on ISIC 2019, outperforming more than 20 deep learning models and literature. These results indicate that the integration of local and global features plays a critical role in accurately classifying skin lesions and can significantly assist dermatologists in clinical practice. © 2025 Elsevier Ltd

关键词： Dermatology

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Research and Development of Kubernetes Operator for Lightweight Multi-Access Edge Computing Platform Simulator

Research and Development of Kubernetes Operator for Lightwei...

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2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Filianin, Ivan Borisov, Nikita Bogatyrev, Vladimir Itmo University Faculty of Software Engineering and Computer Engineering St. Petersburg Russia Itmo University Faculty of Infocommunication Technologies St. Petersburg Russia Saint-Petersburg State University of Aerospace Instrumentation Department of Information Systems Security St. Petersburg Russia

ISBN: (纸本)9798331532635

This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight MultiAccess Edge Computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it. © 2025 IEEE.

关键词： Edge computing

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PathNetDRP: a novel biomarker discovery framework using pathway and protein–protein interaction networks for immune checkpoint inhibitor response prediction

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BMC Bioinformatics 2025年第1期26卷 1-24页

作者： Lee, Dohee Ahn, Jaegyoon Choi, Jonghwan Department of Computer Science and Engineering Incheon National University 119 Academy-ro Yeonsu-gu Incheon22012 Korea Republic of Division of Software Hallym University 1 Hallymdaehak-gil Gangwon-do Chuncheon24252 Korea Republic of

Background: Predicting immune checkpoint inhibitor (ICI) response remains a significant challenge in cancer immunotherapy. Many existing approaches rely on differential gene expression analysis or predefined immune signatures, which may fail to capture the complex regulatory mechanisms underlying immune response. Network-based models attempt to integrate biological interactions, but they often lack a quantitative framework to assess how individual genes contribute within pathways, limiting the specificity and interpretability of biomarkers. Given these limitations, we developed PathNetDRP, a framework that integrates biological pathways, protein-protein interaction networks, and machine learning to identify functionally relevant biomarkers for ICI response prediction. Results: We introduce PathNetDRP, a novel biomarker discovery approach that applies the PageRank algorithm to prioritize ICI-associated genes, maps them to relevant biological pathways, and calculates PathNetGene scores to quantify their contribution to immune response. Unlike conventional methods that focus solely on gene expression differences, PathNetDRP systematically incorporates biological context to improve biomarker selection. Validation across multiple independent cancer cohorts showed that PathNetDRP achieved strong predictive performance, with cross-validation the area under the receiver operating characteristic curves increasing from 0.780 to 0.940. Interestingly, PathNetDRP did not merely improve predictive accuracy;it also provided insights into key immune-related pathways, reinforcing its potential for identifying clinically relevant biomarkers. Conclusion: The biomarkers identified by PathNetDRP demonstrated robust predictive performance across cross-validation and independent validation datasets, suggesting their potential utility in clinical applications. Furthermore, enrichment analysis highlighted key immune-related pathways, providing a deeper understanding of their role in ICI res

关键词： Immune system

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Harnessing the Power of AI with Deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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An Analysis of Anonymous MQTT for Publish-Subscribe-Based IoT Networks

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IEEE Transactions on Network Science and engineering 2025年

作者： Fukushima, Yudai Tsunamoto, Hotaru Sakai, Kazuya Sun, Min-Te Ku, Wei-Shinn Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo191-0065 Japan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States

Message queuing telemetry transport (MQTT) is widely used as a communication primitive in publish-subscribe-based IoT applications. However, the current MQTT standard does not support the privacy of IoT devices and users. Therefore, in this paper, we address the anonymous communications for MQTT-based IoT networks by tailored onion routing. To this end, we first introduce the concept of distributed IoT broker networks, in which a collection of IoT brokers are interconnected with each other. These brokers serve as onion routers. Then, we propose a generic anonymous messaging protocol (A-MQTT) for publish-subscribe-based IoT systems. Unlike typical anonymous communications, there are multiple destination IoT devices, called subscribers, in MQTT contexts. Hence, the privacy notions, such as traceable rate, device anonymity, and path anonymity, are redesigned. To illuminate the fundamental privacy issues, closed-form approximations for these privacy metrics are modeled. In addition, simulation results demonstrate that our A-MQTT significantly improves the security performance and that our proposed models closely approximate the various privacy performance. Furthermore, a proof-of-concept protocol is implemented on a Raspberry Pi 4 in order to show that the proposed A-MQTT can be deployed in an IoT environment. © 2013 IEEE.

关键词： Anonymity

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