The selection of plants able to develop a root system architecture (RSA) optimizing water and nutrient uptake in varying growing conditions has been emphasized as an important target for crop improvement in the perspective of sustainable agriculture. However, due to its belowground localisation and its increasing complexity with plant age, the phenotyping of the root system is challenging, especially in a high-throughput phenotyping context. It requires tools like root image analysis software and automated procedures for the calculation of relevant RSA traits. Data Analysis of Root Tracings (DART) is a free software allowing the manual vectorization of root systems from captured images across time series. For each analysed root system, DART exports temporal, spatial and topological information in three separate files that can further be used for a detailed quantitative analysis of RSA. As the throughput of the data analysis process using DART-generated files should strongly benefit from a batch analysis based on fully automated procedures, we developed an R package (archiDART) allowing the computation of global RSA traits (root length, root number, etc.) at each observation date, root growth directions and trajectories (branching and root tip angles, root curvature), and lateral root distributions from DART output files. Moreover, the dynamics of the root system growth and local root growth rate variations can also be visually assessed using specific mapping functions. The platform DART-archiDART has notably been shown to be an efficient tool to perform a detailed quantitative RSA analysis of a monocotyledonous (Brachypodium distachyon (L.) Beauv.) and two dicotyledonous plant species (Hevea brasiliensis Müell. Arg. and Solanum lycopersicum L.) produced under various growing conditions.