## 5 Conclusions  
This study illustrates the potential of multispectral imaging systems as versatile tools in agricultural settings at close distances. The feasibility of employing stereo vision to correct spectral band misalignment in multispectral imaging, a critical step for reliable vegetation index computation, is presented in a structured workflow.  
A pair of images (red band as a reference and G, B, RE, and NR bands as moving) were processed through the stereo camera calibration algorithm to estimate the target distance from the sensor. The estimated distance was used to back-calculate the offsets required to correct misalignment in all spectral bands and was further applied to crop images, resulting in an aligned multispectral image composed of all bands. From the aligned image, spectral indices correlated with plant health were computed and analyzed.  
This step-by-step approach ensured improved multispectral image processing. The stereo vision approach used for distance estimation showed high accuracy, with average reprojection errors of approximately 0.013 pixels and minimal relative errors (around 5%) for the RE band. The new proposed inverse linear model provided a clearer comprehension of the dependence of geometric offset on sensor distance. Despite some challenges in site-specific band alignment, particularly along the X-axis, the overall performance of the proposed experimental alignment model remained satisfactory, with offset errors between bands remaining below 3 pixels.  
The proposed image alignment method enhances the accuracy of sub-pixel vegetation index calculation in greenhouse environments where short-range imaging is required. Although the results are not yet sufficiently robust for developing a comprehensive predictive model of plant chlorophyll content, the analysis of vegetation indices revealed a clear distinction between healthy and unhealthy plants. This approach holds significant promise for precision agriculture applications, particularly for the continuous monitoring of baby crop health in controlled environments, contributing to more effective and data-driven greenhouse management strategies.  
Conceptualization, S.L., G.A. and G.C.D.R.; methodology, F.G., A.M. and G.C.D.R.; software, G.A.; validation, S.L., F.G. and A.M.; formal analysis, S.L. and L.S.; investigation, S.L. and L.S.; resources, L.S.; data curation, S.L., A.M. and F.G.; writing--original draft preparation, S.L.; writing--review and editing, S.L. and G.A.; visualization, F.G. and A.M.; supervision, G.A.;project administration, G.A. and G.C.D.R.; funding acquisition, G.A. and G.C.D.R. All authors have read and agreed to the published version of the manuscript.  
This study was carried out within the Agritech National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNNR)--MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4--D.D. 1032 17/06/2022, CN00000022). This manuscript reflects only the authors' views and opinions; neither the European Union nor the European Commission can be considered responsible for them.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.