Color vision in digital twin creation using photogrammetry in sustainable agriculture 4.0

Abstract

The study proposes a methodological integration of machine vision and image processing based on color-based object detection. The primary goal of the study is to use the color vision method to simplify the process of transforming real objects into 3D digital twins for application in Sustainable Agriculture 4.0. The experiment solves several related problems: (1) Color analysis and methodology for quantifying the color representation of a 3D model. Representation quality was determined using colorimetric methods with sRGB and L*a*b* models in relation to the D65 standard. Colors with accurate color values on the object surface and in the 3D model were identified. (2) The process of capturing and creating digital twins using the SfM method is time-consuming and requires manual work. The study solves this problem by partially automating the entire process. The proposed DSLR system with an automated method for capturing, storing, and sorting data significantly accelerates the entire process. (3) To create a digital color scale, it is necessary to define the color values of 3D digital twins. A color segmentation procedure based on points on the surface of a 3D model is proposed. These color values form a basic color form corresponding to the color value changes in the coloring process of a real object. The proposed procedure uniquely integrates methodologies and has potential for use in Sustainable Agriculture 4.0. The proposed colorimetric method quantifies representation quality and could be deployed in other 3D model digitization and automation processes, especially in image processing and computer vision.