Mathematical modeling with RGB data for comparative analysis of rice lineages and distribution of breaking points

Rice (Oryza sativa L.) is a crucial food crop, supplying a significant portion of the global population's caloric intake. With the shift from traditional breeding methods to digital approaches, image analysis is becoming essential for distinguishing between rice cultivars. However, the optimal growth stages for effectively utilizing image analysis to classify rice varieties remain uncertain. This study aimed to evaluate 102 rice cultivars through non-destructive image processing and RGB ratio analysis. Images were captured every two days throughout the growth period, and an RGB ratio formula was developed, excluding background pixels to focus on plant characteristics. Regression analysis identified critical time points for differentiation, with the red (R) channel being most effective at 55 and 75 days post-transplanting, and the green (G) channel at 60 and 80 days. Hierarchical clustering of slopes from piecewise regression categorized the 102 cultivars into three distinct clusters, representing their ecological types. These findings provide a precise and efficient method for classifying rice cultivars, offering breeders key insights into the most effective stages for variety differentiation. By optimizing image analysis techniques, this research enhances the efficiency of rice breeding programs and supports the targeted management of genetic resources for improved trait selection.