## 4 Discussion
Such systems could leverage YOLOv8-m's high accuracy to trigger automated cleaning or repair protocols, significantly enhancing grid stability and energy output efficiency. The proposed YOLOv8-m model has the potential to offer a practical solution for the automatic inspection of photovoltaic systems through its real-time inference capability, which operates at a rate of 45 frames per second. However, it is imperative to acknowledge the pivotal role that parallel computing and model optimization techniques play in enhancing processing speed in large-scale solar farms and the field of high-resolution drone imagery. To illustrate this point, consider the potential of spatio-temporal models based on deep reinforcement learning (DRL), which can facilitate concurrent processing of the model across multiple Graphics Processing Units (GPUs) or distributed systems [52]. This approach has the potential to markedly reduce latency and optimize resource utilization, particularly in the context of data processing from video streams. Moreover, drawing inspiration from the research conducted in Travel Demand Forecasting Considering Natural Environmental and Socioeconomic Factors, the present study aims to develop a model that explores the relationship between the geographical distribution of solar panel defects and environmental parameters, such as humidity, temperature, and dust accumulation [53]. Consequently, this approach facilitates proactive prediction of defects and subsequent reduction in maintenance costs. Using fast spatial-temporal compression algorithms facilitates the instantaneous processing of high-dimensional thermal and visual data collected through drone technology [54]. In particular, techniques employed in traffic flow estimation with complex non-linear patterns can reduce the data size by filtering out superfluous background information in solar panel images. This optimizes the training and inference times of YOLO models while improving energy efficiency on IoT devices. Integrating these techniques within a hybrid framework will facilitate the sustainable management of photovoltaic systems, exhibiting high accuracy and low latency. In conclusion, this study underscores the transformative potential of YOLO-based models in solar panel inspection. By combining advanced architectures with robust dataset design, the proposed framework offers a scalable and reliable solution for automating defect detection, thereby contributing to the sustainable management of photovoltaic systems. The comparison in Table 8 includes the results of different studies using different datasets. Despite being regarded as a methodological limitation, the primary objective of this study is to introduce a novel dataset to the existing literature and to assess the efficacy of deep learning models on this dataset. Most studies in the extant literature utilize datasets with limited diversity or are inaccessible to the public. The proposed SPHERE dataset (6079 images) is characterized by its coverage of various environmental conditions (light, angle, panel orientation), high-resolution samples, real-world scenarios, and SPHERE public access. This distinction renders the model's performance in real-world applications more dependable. Nevertheless, the principal contribution of this study is to provide a new dataset and establish a standard benchmark for future research. The 97.26% accuracy of YOLOv8-m obtained in this study on this dataset will be a reference point for future models to be tested using the same set. Moreover, the heterogeneity of the dataset engenders an optimal milieu for assessing model generalization. In conclusion, the present study aims to address a data-driven gap in the field of photovoltaic defect detection and to pave the way for the development of innovative solutions.