A Novel Method for Calibration of Digital Soil Images Captured under Irregular Lighting Conditions

Abstract

Soil color is commonly used as an indicator to classify soil and identify its properties. However, color-based soil assessments are susceptible to variations in light conditions and the subjectivity of visual evaluations. This study proposes a novel method of calibrating digital images of soil, regardless of lighting conditions, to ensure accurate identification. Two different color space models, RGB and CIELAB, were assessed in terms of their potential utility in calibrating changes to soil color in digital images. The latter system was determined to be suitable, as a result of its ability to accurately reflect illuminance and color temperature. Linear regression equations relating soil color and light conditions were developed based on digital images of four different types of soil samples, each photographed under 15 different light conditions. The proposed method can be applied to calibrate variations in the soil color obtained by digital images, thus allowing for more standardized, objective, and accurate classification and evaluation of soil based on its color.

Customized Integrating-Sphere System for Absolute Color Measurement of Silk Cocoon with Corrugated Microstructure

“…It achieves this by utilizing standard color calibration charts to linearly align pixel values from the image with the standardized color values of the calibration chart [ 38 ]. Previous studies emphasize color calibration as one of the most important factors to ensure color measurement accuracy and minimize error [ 39 , 40 ]. In a study by researchers in [ 41 ], precise RGB spectral response functions were predicted using ColorChecker, demonstrating minimal root mean square error between the actual values and their recovered RGB spectral response, all achieved through color calibration.…”