Autonomous underwater vehicles (AUVs) play a crucial role in oceanographic research, monitoring the environment, and exploring resources in the ocean. Nevertheless, the operational efficiency of these devices is frequently constrained by the limited battery capacity and the requirement for charging while connected to a power source. Wireless power transfer (WPT) offers a non-contact alternative to conventional wet-mate electrical connectors, with inductive coupling receiving particular attention because of its relatively high efficiency, safety, and suitability for underwater charging over short transfer gaps. However, it is limited by the transfer distance, coil misalignment, coupler design constraints, and eddy-current losses caused by conductive seawater. This review assesses the current state of inductive coupling technology with respect to AUVs. It discusses recent advancements in energy sources for AUVs, current technologies for underwater WPT, magnetic coupler design, challenges related to misalignment and their impact on WPT systems, as well as various strategies to mitigate these challenges. The review also covers the analysis and mitigation of eddy current loss and highlights the technical and engineering challenges encountered in power delivery. In general, inductive WPT has significant potential for making AUV charging performance more successful. Experimental studies have indicated efficiencies exceeding 90% in certain controlled situations; however, the literature also demonstrates that performance frequently deteriorates under misalignment and inadequate underwater conditions, which remain major challenges to practical implementation.

Healthcare, education, technological advancement, and farming are the key challenges facing developing countries, with agriculture unquestionably playing an important role in economic growth. Ensuring adequate food production is essential for citizens’ survival, as it is anticipated that efforts in this area would result in increased food productivity. A key approach to enhancing field productivity involves meticulous care of its components, starting with the production of crops. Wheat leaf rust poses a severe threat, particularly to young seedlings, constituting a significant fungal disease that can cause a 25% reduction in wheat productivity. To overcome these issues, this research work proposes a novel image fusion approach called Multi-Scale Discrete Wavelet Transform (MS-DWT). The method uses distinct fusion strategies to extract meaningful details from source images. After that, a Lab Color Space (LCS), followed by a color thresholding method, is employed for the detection and lesion localization of rust in the source images. Furthermore, the proposed model measures the area affected by rust in wheat crops, providing farmers with vital information during the post-medication (anti-rust spray) operation. The experimental findings demonstrate superior performance and achieve a classification accuracy of 98.85%, and the maximum testing accuracy was 99.17% on our generated dataset.