A Review on Artificial Intelligence-Based Visualization and Cleaning of PV Panels Using Uncrewed Air Vehicle for Desert Plants in Oman

Oman is located on the southeastern coast of the Arabian Peninsula in West Asia, and the Middle East has a valleys and desert region of 82% of its land area. This desert area has significant potential for solar energy harvesting because of its dry nature and flat landscapes. One of the biggest obstacles to optimising solar energy harvesting efficiency in desert PV plants is cleaning photovoltaic (PV) panels. The panels' surface becomes covered in dirt, dust, and other environmental contaminants, significantly lowering their power production. The increasing reliance on photovoltaic (PV) systems for sustainable energy production necessitates efficient maintenance solutions to address performance losses caused by dirt and debris accumulation. Even though several cleaning techniques have been created, research is still ongoing to determine the best one. Artificial intelligence (AI) is revolutionizing industrial processes today, and cleaning solar panels is one promising area for its implementation. This report explores the review of the design and implementation of an artificial intelligence (AI) powered, un-crewed aerial vehicle (UAV) system to revolutionise the cleaning and maintenance of PV panels. Typically, this system integrates advanced image processing algorithms to detect contaminants and deploys precision-controlled UAV mechanisms to perform targeted cleaning operations, minimising resource usage and maximising efficiency. The study outlines a search for a comprehensive methodology encompassing equipment selection, AI algorithm development, UAV navigation optimisation, and sustainability considerations. A proposal is given in the end to get outcomes from the study, including a significant reduction in water and chemical usage, enhanced cleaning accuracy, and the restoration of up to 30% of lost energy output in contaminated panels. Comparative analysis with traditional cleaning methods is also highlighted to reduce costs, labour dependency, and environmental impact.