Quantifying plant structural complexity is critical for understanding genotype-to-phenotype relationships in modern plant breeding and phenotype. Traditional approaches—such as Leaf Area Index (LAI) or Fractal Dimension (FD)—often focus on isolated traits and lack the capacity to capture the holistic architecture of plants. In this study, a framework has been developed for evaluating plant structure through a Structural Complexity Index (SCI). The SCI integrates both skeleton-based and shape-based morphological features to provide a comprehensive representation of plant architecture. In order to manage the high dimensionality of the extracted feature set, the Principal Component Analysis (PCA) technique has been applied. The first principal component captures the maximum variance across all samples, which was used as a unified SCI score. This allowed for efficient quantification of structural complexity across genotypes and growth stages. Furthermore, the validation of the biological relevance of the top contributing features to SCI has been analyzed statistically, specifically using ANOVA, to assess significant differences across growth stages. In this work, mustard plant data has been used for validation. It is found that this framework enables accurate, scalable, and non-invasive characterization of plant architecture. It supports high-throughput phenotyping pipelines by offering a standardized and interpretable complexity metric, making it a valuable tool for plant scientists engaged in phenotyping, developmental analysis, and genetic research.