[Objectives] Seed is one of the most important research topics in plant research. The ability of dynamically detecting key seed germination traits provides important phenotypic evidence for researchers to understand plant survival, growth, development, and reproduction. Here, we proposed a set of algorithms for quantifying germination-related traits by combining automated image analysis, graph theory and supervised machine learning techniques. [Methods] Utilizing Poaceae such as wheat (Triticum aestivum) as a model plant, we applied automated image analysis together with machine learning algorithms (e.g. K-Nearest Neighbors, Support Vector Machine, Random forests) to train foreground and background objects, followed by background segmentation and object extraction based on image series collected from three weak gluten wheat varieties. Then, graph theory and two-dimensional skeletonization were employed to dynamically analyze changes of radicles and radicle tip positions to measure key germination-related traits in a high- throughput manner. [Results] We have collected a range of phenotypic traits in this study that were difficult to obtain through traditional approaches, including seed length, width, area, perimeter, radicle and seedling length, and their growth rates. We applied a linear regression analysis to validate the computational results with manual scoring, the square of the correlation coefficient, R2, computed for traits such as radical length, radical growth rate and seedling length are 0.922 (n=188, P<0.001, ，RMSE=1.727), 0.719 (n=191, P<0.001, RMSE=0.406), 0.897 (n=115, P<0.001, RMSE=2.726), respectively. [Conclusions] The results suggest that the algorithm and open-source software presented here can reliably obtain dynamic seed germination traits, which can also be extended to other crop species such as cotton (Gossypium barbadense) and oilseed rape (Brassica napus), providing phenotypic evidence and smart analytic solutions to enable studies in plant genetics and crop breeding
