Crop-saving with AI: latest trends in deep learning techniques for plant pathology

Plant diseases pose a major threat to agricultural production and the food supply chain, as they expose plants to potentially disruptive pathogens that can affect the lives of those who are associated with it. Deep learning has been applied in a range of fields such as object detection, autonomous vehicles, fraud detection etc. Several researchers have tried to implement deep learning techniques in precision agriculture. However, there are pros and cons to the approaches they have opted for disease detection and identification. In this survey, we have made an attempt to capture the significant advancements in machine-learning based disease detection. We have discussed prevalent datasets and techniques that have been employed as well as highlighted emerging approaches being used for plant disease detection. By exploring these advancements, we aim to present a comprehensive overview of the prominent approaches in precision agriculture, along with their associated challenges and potential improvements. This paper delves into the challenges associated with the implementation and briefly discusses the future trends. Overall, this paper presents a bird’s eye view of plant disease datasets, deep learning techniques, their accuracies and the challenges associated with them. Our insights will serve as a valuable resource for researchers and practitioners in the field. We hope that this survey will inform and inspire future research efforts, ultimately leading to improved precision agriculture practices and enhanced crop health management

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research

Milled Microchannel-Assisted Open D-Channel Photonic Crystal Fiber Plasmonic Biosensor

Funding Information: This work was supported by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science) under Grant ICT-NRF-2020R1A2B5B02002478.Peer reviewedPublisher PD

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed

Providing End-to-End Security Using Quantum Walks in IoT Networks

Internet of Things acts an essential role in our everyday lives and it definitely has the potential to grow on the importance and revolutionize our future. However, the present communication technologies have several security related issues which is required to provide secure end to end connectivity among services. Moreover, due to recent, rapid growth of quantum technologies, most common security mechanisms considered secure today may be soon imperilled. Thus, the modern security mechanisms during their construction also require the power of quantum technologies to resist various potential attacks from quantum computers. Because of its characteristics, quantum walks (QW) is considered as a universal quantum computation paradigm that can be accepted as an excellent key generator. In this regard, in this paper a new lightweight image encryption scheme based on QW for secure data transfer in the internet of things platforms and wireless networking with edge computing is proposed. The introduced approach utilises the power of nonlinear dynamic behaviour of QW to construct permutation boxes and generates pseudo-random numbers for encrypting the plain image after dividing it into blocks. The results of the conducted simulation and numerical analyses confirm that the presented encryption algorithm is effective. The encrypted images have randomness properties, no useful data about the ciphered image can be obtained via analysing the correlation of adjacent pixels. Moreover, the entropy value is close to 8, the number of the pixel change rate is greater than 99.61%, and there is high sensitivity of the key parameters with large key space to resist various attack

Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment

Sentiment summarization is the process of automatically creating a compressed version of the opinionated information expressed in a text. This paper presents a machine learning-based approach to summarize user's opinion expressed in reviews using: (1) Sentiment knowledge to calculate a sentence sentiment score as one of the features for sentence-level classification. It integrates multiple strategies to tackle the following problems: sentiment shifter, the types of sentences and word coverage limit. (2) Word embedding model, a deep-learning-inspired method to understand meaning and semantic relationships among words and to extract a vector representation for each word. (3) Statistical and linguistic knowledge to determine salient sentences. The proposed method combines several types of features into a unified feature set to design a more accurate classification system (“True”: the extractive reference summary; “False”: otherwise). Thus, to achieve better performance scores, we carried out a performance study of four well-known feature selection techniques and seven of the most famous classifiers to select the most relevant set of features and find an efficient machine learning classifier, respectively. The proposed method is applied to three different datasets and the results show the integration of support vector machine-based classification method and Information Gain (IG) as a feature selection technique can significantly improve the performance and make the method comparable to other existing methods. Furthermore, our method that learns from this unified feature set can obtain better performance than one that learns from a feature subset