## 1 Introduction
Several vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Red Edge Index (NDRE), the Anthocyanin Reflectance Index (ARI), and others, have been developed using multispectral signatures, which can be used to evaluate plant health [16; 17; 18] or deficiencies in water or nutrients [8; 10; 11; 12; 23]. Hence, prevention of crop yield losses and early detection of plant stresses are the goals of a multispectral imaging system that can automate agricultural management [14]. Although the integration of sensors is considered one of the major advantages underlying short-range phenotyping platforms [14], the acquisition of images from one or more sensors or different points of view has emerged as an open challenge in the field of image analysis [1]. Misalignment is particularly problematic when using low-altitude imaging systems that capture images from non-orthogonal angles relative to the plant surface. This can lead to significant errors in spectral index calculations, compromising the quality of the analysis and, consequently, the accuracy of crop health diagnoses [24]. Related Work  
Recent studies have highlighted the relevance of image registration methods in the case of non-georeferenced multispectral images acquired at close range, for example, to identify the presence of powdery mildew on cucumber plants [25], water stress on lettuce [23], identification of weeds in wheat [26], and the effects of potassium water deficit in cassava [27]. The Scale-Invariant Feature Transform (SIFT) algorithm [27] and Speeded-Up Robust Features (SURF) [28] are commonly used for feature detection, based on the alignment of fixed and moving image spaces. One of the main advantages of these methods is that they are invariant to scale changes. SIFT is also invariant to orientation and illumination [29]; however, it struggles with processing time constraints. In contrast, SURF was developed to overcome this issue [30]. With the increasing use of computer vision, other feature detection techniques have been proposed, such as KAZE, AKAZE, ORB, and BRISK, with an overview and comparison provided by [31]. Although registration techniques differ in preserving image parameters like scale, angles, etc., they are based on affine transformations. An affine transformation combines translation, rotation, scaling, and image shearing (parallel distortion). According to the comparative analysis proposed by [32], the affine transformation was found to be the most accurate in terms of multispectral image registration accuracy (RMSE \(<\) 1 pixel). Applying automatic image registration techniques has been the focus of several researchers, who have found success using open-source programming. Different sensor perspectives have been used to test registration algorithms in complex ecosystems [28] and on individual plants [27]. As previously discussed in [24], there is a lack of a metric to adjust the registration offset of images related to changes in the sensor acquisition distances. Therefore, the main objective of this work was to further explore the implications of sensor acquisition distance in the calibration of multispectral imaging systems by addressing the gaps in image alignment. Specifically, this study focused on developing a robust model to quantify and compensate for alignment discrepancies from different sensor acquisition distances and evaluate the applicability to physiological stress analysis in crops. Furthermore, to extend the utility of multispectral imaging in agriculture, this work investigated the potential of stereo vision applied to multispectral images to propose a single automated system to calculate the distance between the target and the sensor in real time.