Drones in 6G Network Environment Achieve Dynamic Detection of Water Environment by Collecting High-Resolution Images

With the introduction of 6G connections and developments in the field of drones, there is an increasing chance to change monitoring of the surroundings, especially in the position of water quality evaluation. Previous water quality index algorithms evaluate water quality utilising a variety of methods of categorisation. Various methodologies offer different views of water attributes, leading to difficulty in determining an accurate evaluation of water quality. This examination specifies a novel technique for dynamic underwater identification based on the capability of drones working in a 6G network. The suggested method applies the you only look once (YOLO) object detection algorithm to high-resolution drone photos, allowing for real-time recognition and evaluation of water quality parameters. Initially, the drone with high-resolution cameras captured high-quality images of the water surrounding. The drone transmits the data with the help of 6G networks and stores it in cloud environments. Next, the YOLO with CNN method is used to recognise and monitor the different water qualities, such as pollutants, algae blooms, and debris, dynamically. The deployment of drones with YOLO-CNN efficiently monitors the water environment. The results of the study demonstrated that, when it comes to accurate categorisation, the YOLO method may be a valuable and trustworthy tool for evaluating the quality of coastal waters. As a result, YOLO with CNN model achieves 91% of accuracy is prediction of water quality.