## 2 Materials and Methods  
Wind power generation plays a crucial role in transitioning to renewable energy sources. However, wind turbines are exposed to adverse environmental conditions that can lead to wear and failure of critical components such as blades. Early detection of these anomalies is essential to ensure the efficient operation and safety of wind turbines.  
Wind turbine blades are subject to mechanical stresses and variable weather conditions, which can result in cracks, deformations, or even fractures. Early detection of these failures is essential to avoid costly interruptions in power generation and to ensure the safety of the infrastructure. Conventional inspection techniques often require periodic, manual shutdowns, which entails additional costs and limits the frequency of revisions.  
Early detection of blade failures not only contributes to the operational efficiency of wind turbines but also reduces costs associated with unplanned maintenance and extensive repairs. In addition, worker safety is enhanced by minimizing the need for physical inspections in potentially hazardous conditions, such as high altitudes and harsh weather environments.  
Currently, techniques and methods for digital image processing are widely used to manipulate, extract, enhance, or represent features from a human perspective. The primary tool used in digital image processing is mathematics, often complemented by various disciplines such as computer science, artificial intelligence, machine learning, computer vision, control, optimization, electrical engineering, and biology. These techniques and methods are implemented in a wide range of applications, including UAVs in aeronautics [30], object observation and exploration in robotics [31], simulation and animation [32], diagnosis [33], and evaluation without the need for invasive methods [34], among others.  
In this work, 100 images of wind turbine blades were used, taken from a wind farm located in the Universidad del Istmo area in the Mexican state of Oaxaca. These images include both damaged and undamaged blades.  
A digital image is a two-dimensional array or matrix representing the discrete sampling of a continuous signal, where each cell of the matrix is called a pixel and may be constructed of one or multiple channels [35]. Mathematically, such a two-dimensional array can be represented by the Equation (1).  
\[f(x,y) \tag{1}\]  
where the ordered pair (\(x,y\)) represents coordinates in a spatial plane, and \(f\) is called the intensity or gray level at any pair of (\(x,y\)) coordinates in the image. A two-dimensional image \(f(x,y)\) is described by a series of equally spaced samples in the form of an \(N*M\) matrix, where each element of the matrix is a discrete quantity according to the pair of \(N\) (columns) and \(M\) (rows).  
Figure 2 shows the position (\(x,y\)) in a matrix and its value in three channels that make up the image: RGB (red, green, blue).  
Thus, the value of each pixel is a discrete number belonging to the domain of positive integers. In a grayscale image, the value of each pixel (resolution) \(G\) is represented as Equation (2).  
\[G=2^{k}-1 \tag{2}\]  
The gray ranges between black and white (\(G\)) in an image of \(N\) pixels wide by \(M\) pixels long with \(k\) bits of resolution. The space required to store the image is calculated as the product of these three variables, i.e., \(N\times M\times k\).