Construction of Response Models for Color Gradation Skewed Distribution Parameters Extracted from Digital Wheat Canopy Images in Response to Cold-Spell Effects

Abstract

This study examined the response of color information in digital wheat canopy images from Shandong Province, China, to meteorological indicators during extreme cold spells. Analysis revealed that low-temperature stress altered pixel color and grayscale values, with shifts captured by skewness and kurtosis parameters of color gradation distributions. The kurtosis and skewness of color gradient distributions showed the strongest sensitivity to cold stress. Daily minimum temperature was significantly correlated with kurtosis values for R (0.661), G (0.744), B (0.694), and grayscale (0.744) channels. Models relating these parameters to meteorological factors were developed, with polynomial functions outperforming multilinear approaches. All models demonstrated satisfactory fit, as evidenced by determination coefficients exceeding 0.480. The kurtosis model for green values achieved exceptional prediction accuracy, surpassing 90%. Findings demonstrate quantifiable cold-induced changes in canopy color gradient distribution, establishing a foundation for enhancing freeze damage monitoring systems through image-based metrics. These models enable efficient early warning by linking meteorological data to visible canopy responses, offering practical tools for mitigating agricultural cold stress impacts.