Correlation and path analysis for seed morphometric as well as yield-related traits were performed on five bread wheat varieties, each sown on an area of one acre; this investigation was carried out to check the influence of these traits on the grain yield of bread wheat (Triticum aestivum L.). Digital image analysis (DIA) is a quantitative technique to phenotype seed morphometric characteristics with high accuracy. Multivariate analysis was performed to determine the relationship bdeetween ground cover (GC) and seed size and grain yield (GY). Significantly important traits were identified by principal component analysis (PCA). Applying the multiple linear regression model, grain yield was predicted with the help of ground cover and seed shape traits. The association of the studied traits was quantified with a structural equation model. The impact of spikes per meter square (SPMS) and thousand kernel weights (TKW) on grain yield was significant (p-value <.01). Plant height has a detrimental impact on grain yield, and the grain yield was influenced by two features: spikes per meter square (SPMS) and grain weight per spike (GWS), which have a direct impact on thousand kernel weight (TKW). The present research aims to feature the relationship between yield-contributing characteristics and the digital ground cover of the wheat crop. The present work uncovered the fact that digital ground cover (DGC) influences the seed length, area, and perimeter.