## 2 Materials and Methods  
### Stereo Camera Calibration  
Camera calibration aims to establish the transformation from the three-dimensional (3D) real world to two-dimensional (2D) digital images. In general, the simplest case assumes that the camera operation can be described by an ideal pinhole model, known as the Pinhole model [33]. Briefly, by using a pattern with known dimensions, a reference point system is defined in the real world as \(P_{1}\), \(\ldots\), \(P_{n}\). When these points are projected onto the image captured by the camera, they generate corresponding image points \(p_{1}\), \(\ldots\), \(p_{n}\). The relationship between points in 3D space, \(P_{i}=[X_{i},Y_{i},Z_{i},1]^{T}\), and their corresponding points in the image, \(p_{i}=[x_{i},y_{i},1]^{T}\), excluding the effects of translation (\(T\)) and rotation (\(R\)), is described by the camera matrix \(M\). In its simplified form, the relationship between \(P_{i}\) and \(p_{i}\) is expressed as \[p_{i}\cong M\cdot P_{i} \tag{1}\]  
where \(p_{i}\) represents the homogeneous coordinates of the point in the 2D image, \(P_{i}\) represents the homogeneous coordinates of the point in 3D space, and \(M\) is a (3 \(\times\) 4) matrix describing the camera projection. This matrix combines both intrinsic parameters (which describe the optical and geometric properties of the camera) and extrinsic parameters (which define the camera's position and orientation relative to the scene), as follows:  
\[M=K\cdot[R|T] \tag{2}\]  
where \(K\) is a (3 \(\times\) 3) matrix containing the intrinsic parameters, \(R\) is a (3 \(\times\) 3) rotation matrix that defines the camera's orientation, and \(T\) is a (3 \(\times\) 1) translation vector that represents the camera's position. Similarly, when working with a multicamera (stereo) system, it is possible to determine the geometric relationship (rotation and translation) between the two captured images [34]; this process is known as stereo calibration. Stereo calibration is commonly used in autonomous vehicle navigation to estimate the geometric and optical parameters related to the position of the stereo system [35]. As is well known, the relationship between homologous points in the left and right images of a stereo pair (known as disparity (\(d\))) allows for the determination of the depth (\(Z\)) of the captured scene. This is given by the equation  
\[Z=f\times B/d \tag{3}\]  
where \(f\) is the focal length of the sensor, \(B\) is the baseline distance between the camera pair, and \(d\) is the disparity. Experimental images of a checkerboard pattern (35 mm squares, printed on an A3 sheet and fixed to a rigid plywood surface) were acquired at distances ranging from 500 mm to 1500 mm, in 100 mm steps. For each distance, five different image positions were captured using a MicaSense RedEdge P\({}^{\text{TM}}\) multispectral (MS) camera (AgEagle Aerial Systems Inc., Wichita, KS, USA). This camera featured a sensor resolution of 1456 \(\times\) 1088 pixels (1.6 MP per band) and captured images in five spectral bands: blue (B) (475 nm \(\pm\) 32 nm), green (G) (560 nm \(\pm\) 27 nm), red (R) (668 nm \(\pm\) 14 nm), red edge (RE) (717 nm \(\pm\) 12 nm), and NIR (NR) (842 nm \(\pm\) 57 nm). The proposed binocular system (Figure 1) utilized the red (R) band sensor as a fixed reference lens, while the sensors of the other spectral bands (G, B, NIR, and RE) functioned as relative lenses. Figure 1: Location of the optical lens of the MicaSense RedEdge P sensor and relative pose of the spectral bands (B, G, NR, and RE) to the R band, with distance from the reference lens (cm). The acquired images were processed using the flowchart proposed by [36] within the Camera Calibration Toolbox in MATLAB software (2023b) [37]. The stereo calibration process was implemented using the \"_estimateCameraParameters_\" function, which enabled the calculation of the geometric parameters (\(R\), \(T\), relative pose of the cameras, and reprojection error) for each image pair.