This study experimentally investigates the effectiveness of various PV cleaning techniques for potential retrofitting into unmanned aerial vehicles, drones, for large-scale solar power plant cleaning operations in desert climates. The ultimate objective of this study is to improve the efficiency, reduce the cost, time and environmental impact of cleaning techniques by integrating them with autonomous drone cleaning to ensure cost-effectiveness and competitiveness of Solar PV installations. This study considers the following factors: (i) cleaning effectiveness (power output change in W and %), (ii) frequency of cleaning (daily, weekly, and monthly), (iii) overall cost of cleaning (USD/m(2)), (iv) adaptability to drone, (v) energy consumption, and (vi) weight. The cleaning techniques tested for their effectiveness and overall cost are (1) cloth wipers, (2) brushes, (3) vacuum cleaners, and (4) some combinations. Cleaning frequency is varied from 1-day to 1-month intervals to find the optimal cleaning period. The results of this study show that there is a significant reduction in the PV efficiency, and hence an increase in the cost of produced electricity, for monthly cleaning periods regardless of cleaning technique in desert climate conditions. The microfiber based-cloth wiper is found to be the optimum method from both cost and performance point of view. Microfiber based-cloth wiper achieved on average 7.7% and 3.1% performance improvement (compared to control panel) for weekly cleaning frequency in winter and summer seasons, respectively. The assessment of drone retrofitting was also conducted, which resulted that the brush and microfiber based-cloth wiper with their low weight, small size, and ease of use are best-suited options for drone-based solar panel cleaning. The evaluation factors for microfiber based-cloth wiper are found to be (i) 3.1% for cleaning effectiveness in summer, (ii) 1 week for frequency of cleaning, (iii) 0.41 USD/m(2) for overall cost of cleaning, (iv) 9.5 (out of 10) for adaptability to drone, (v) 10 (out of 10) for energy consumption and (vi) 9 (out of 10) for weight.