Agriculture can be regarded as the backbone of human civilization. As technology evolved, the synergy between agriculture and remote sensing has brought about a paradigm shift, thereby entirely revolutionizing the traditional agricultural practices. Nevertheless, the adoption of remote sensing technologies in agriculture face various challenges in terms of limited spatial and temporal coverage, high cloud cover, low data quality and so on. Industry 5.0 marks a new era in the industrial revolution, where humans and machines collaborate closely, leveraging their distinct capabilities, thereby enhancing the decision making capabilities, sustainability and resilience. This paper provides a comprehensive survey on remote sensing technologies and related aspects in dealing with the various agricultural practices in the Industry 5.0 (I5.0) era. We also elaborately discuss the various applications pertaining to I5.0- enabled remote sensing for agriculture. Finally, we discuss several challenges and issues related to the integration of I5.0 technologies in agricultural remote sensing. This comprehensive survey on remote sensing for agriculture in Industry 5.0 era offers valuable insights into the current state, challenges, and potential advancements in the integration of remote sensing technologies and Industry 5.0 principles in agriculture, thus paving the way for future research, development, and implementation strategies in this domain.

Industry 5.0 has become a reality now and it is a paradigm that integrates contemporary innovations and concepts. Artificial Intelligence (AI) is a key component and asset of the industrial transformation which allows intelligent devices to perform functionalities such as self-examination, assessment, and evaluation autonomously. AI-based methodologies using ML and deep learning assist manufacturers and industrialists in forecasting service requirements and minimizing downtime. Recent research has discovered a remarkable change in the processes, systems, applications, and products in industries. Also, there is a significant challenge with the explainability of the decisions provided by the models using deep learning algorithms and their inadequate ability to be coupled with each other. Therefore, Explainable artificial intelligence (XAI) is required without compromising the efficiency of the models developed using deep learning algorithms. XAI investigates and develops algorithms, techniques, and models that produce human-comprehensible explanations of AI-based systems and can increase transparency and performance. The explainability nature of XAI will help humans understand the model and the reason behind the predictions, thus improving the model’s transparency and the reliability of the outcomes. Furthermore, an Industry 5.0-enabled environment has a variety of data from varied sources, and this multi-source information must be fused to derive meaningful and optimal decisions. Therefore, all AI-integrated applications must derive actionable insights through information fusion. Hence, the adoption of XAI methodologies in Industry 5.0 can help humans make trustworthy decisions for critical applications requiring information fusion. In this paper, we present a state-of-the-art survey on adopting XAI in Industry 5.0. We discuss the adoption of XAI in various applications such as smart factories, smart Healthcare, E-Governance, smart transportation, Education 5.0, Agriculture 5.0, and Energy 5.0. Finally, some research issues and future directions of integrating XAI with Industry 5.0 are also discussed and highlighted to promote more study in the potential field.

Nowadays, most of the doubly fed induction generators (DFIGs) are equipped with rotor crowbar for the requirement of low voltage ride through (LVRT). The crowbar resistance is an important parameter, and it is selected taking rotor overcurrent and dc link overvoltage limits into consideration. However, the impact of grid impedance on the LVRT performance of DFIG and crowbar resistance is not adequately researched. This paper proposed an improved method to analyze the LVRT performance of DFIG system with rotor crowbar taking the influence of the grid impedance to fill this gap. The impedance of the present grid would be decreased in the future due to the installation of more wind farms to the grid. As a consequence, the performance of DFIG under LVRT with rotor crowbar is degraded. So the design rules of the crowbar resistance need to be reconstructed. Additionally, the analytical expression of the crowbar resistance is derived considering the grid impedance. The effectiveness of the proposed method is validated through theoretical analysis and MATLAB simulations.

Autonomous decision-making is considered an intercommunication use case that needs to be addressed when integrating open radio access networks with mobile-based 5G communication. The robustness of innovations is diminished by the conventional method of designing an end-to-end radio access network solution. Through an analysis of these possibilities, this paper presents a machine learning-based intelligent system whose primary goal is load balancing using Artificial Neural Networks with Particle Swam Optimization-enabled metaheuristic optimization mechanisms for telecommunication industry requests, like product compatibility. We increase the proposed system’s reliability by using third-generation partnership project standards to automate the distribution of transactional load among various connected units. This intelligent system encloses the hierarchy of automation enabled by artificial intelligence. Conversely, AI-enabled open radio access control explores the barriers to next-generation intercommunication, including those after 5G. It covers deterministic latency and capabilities, physical layer-based dynamic controls, privacy and security, and testing applications for AI-based controller designs.

In today's digital world, with growing population and increasing pollution, unhealthy lifestyle habits like irregular eating, junk food consumption, and lack of exercise are becoming more common, leading to various health problems, including kidney issues. These factors directly affect human kidney health. To address this, we require early detection techniques that rely on text data. Text data contains detailed information about a patient's medical history, symptoms, test results, and treatment plans, giving a complete picture of kidney health and enabling timely intervention. In this research paper, we proposed a range of sophisticated models, such as Gradient Boosting Classifier, Light GBM, CatBoost, Support Vector Classifier (SVC), Random Boost, Logistic Regression, XGBoost, Deep Neural Network (DNN), and an Improved DNN. The Improved DNN demonstrated exceptional performance, with an accuracy of 90 %, precision of 89 %, recall of 90 %, and an F1-Score of 89.5 %. By combining traditional machine learning and deep neural networks, this integrative approach enables the identification of intricate patterns in datasets. The model's data-driven processes consistently update internal parameters, guaranteeing flexibility in response to evolving healthcare settings. This research represents a notable advancement in the progress of creating a more detailed and individualised ability to diagnose kidney stones, which could potentially lead to better clinical results and patient treatment.

Internet of Things (IoT) devices along with associated software have proliferated at an unprecedented pace, presenting the challenge of high energy use combined with latency during complex, time-sensitive transactions. Fog computing, i.e., a distributed computing paradigm, may be a potential remedy. However, despite these efforts, it is highly strenuous to regulate service latency and energy efficiency within the fog computing layer for IoT centered consumer electronics. In our research, we propose an algorithm called Dynamic Deep Reinforcement learning-based Task Offloading (DDTO). Therefore, in multi-modal IoT-Fog systems, the intelligent distribution of fog computing resources by DDTO is considered on the basis of resources constraints and the timeliness of completion of tasks. We use a log-normal distribution to describe delay and energy consumption so as to make up for varying modalities. Besides, a task prioritization problem, which is described as an integer programming problem, that minimizes service latency and energy consumption in fog servers is further described. DDTO yields better performance than a conventional Q–learning with respect to long-term expected rewards since it uses task priority weights derived from statistical latency and energy data. Experimental results demonstrate the benefits of DDTO on reducing service latency and consumption of energy, when compared to benchmark strategies, discussing issues with multiple modalities within IoT-Fog systems.