The global installation capacity of solar photovoltaic (PV) systems is exponentially increasing. However, the accumulation of soil and debris on solar panels significantly reduces their efficiency, necessitating frequent cleaning to maintain optimal energy output. This study presents a deep learning-based approach for the recognition and classification of soiled PV images, aimed at enhancing the capabilities of solar cleaning robots through the HALCON software framework. Using EANN and CNN architecture along with advanced image processing techniques, the proposed system achieves precise detection and classification of soiling patterns. The HALCON framework facilitates image acquisition, preprocessing, segmentation, and deployment of trained models for robotic control. The trained models demonstrate exceptional accuracy, with the EANN and CNN achieving classification precision of 99.87% and 99.91%, respectively. Experimental results highlight the system’s potential to improve automation of cleaning strategies, reduce unnecessary cleaning cycles, and enhance the overall performance of solar panels. This research underscores the transformative role of intelligent visual analysis in optimizing maintenance practices for renewable energy applications.

In the context of harvesting tidal stream energy, which is considered a promising source of renewable energy due to its high energy density, stability, and predictability, this paper proposes a review-based roadmap investigating the use of data-driven techniques, more specifically machine learning-based approaches, to detect and estimate the extent of biofouling in tidal stream turbines. An overview of biofouling and its impact on these turbines will be provided as well as a brief review of current methodologies and techniques for detecting and estimating biofouling. Additionally, recent developments and challenges in the field will be examined, while providing several promising prospects for biofouling detection and estimation in tidal stream turbines.

Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of automated systems for efficient monitoring. The study presented in this paper focuses on detecting biofouling on tidal stream turbine blades using camera-based monitoring. The process begins with dividing the video into a series of images, which are then annotated to identify and select the bounding boxes containing objects to be detected. These annotated images are used to train YOLO version 8 to detect biofouled and clean blades in the images. The proposed approach is evaluated using metrics that demonstrate the superiority of this YOLO version compared to previous ones. To address the issue of misdetection, a data augmentation approach is proposed and tested across different YOLO versions, showing its effectiveness in improving detection quality and robustness.