Image-based plant phenotyping analysis refers to the monitoring and quantification of phenotyping traits by analyzing images of the plants captured by different types of cameras at regular intervals in a controlled environment. Extracting meaningful phenotypes for temporal phenotyping analysis by considering individual parts of a plant, e.g., leaves and stem, using computer-vision based techniques remains a critical bottleneck due to constantly increasing complexity in plant architecture with variations in self-occlusions and phyllotaxy. The paper introduces an algorithm to compute the stem angle, a potential measure for plants' susceptibility to lodging, i.e., the bending of stem of the plant. Annual yield losses due to stem lodging in the U.S. range between 5 and 25%. In addition to outright yield losses, grain quality may also decline as a result of stem lodging. The algorithm to compute stem angle involves the identification of leaf-tips and leaf-junctions based on a graph theoretic approach. The efficacy of the proposed method is demonstrated based on experimental analysis on a publicly available dataset called Panicoid Phenomap-1. A time-series clustering analysis is also performed on the values of stem angles for a significant time interval during vegetative stage life cycle of the maize plants. This analysis effectively summarizes the temporal patterns of the stem angles into three main groups, which provides further insight into genotype specific behavior of the plants. A comparison of genotypic purity using time series analysis establishes that the temporal variation of the stem angles is likely to be regulated by genetic variation under similar environmental conditions.