Root architecture, which determines the water and nutrient uptake ability of crops, is highly plastic in response to soil environmental changes and different cultivation patterns.  Root phenotyping for field-grown crops, especially topological trait extraction, is rarely performed.  In this study, an image-based semi-automatic root phenotyping method for field-grown crops was developed.  The method consisted of image acquisition, image denoising and segmentation, trait extraction and data analysis.  Five global traits and 40 local traits were extracted with this method.  A good consistency in 1st-order lateral root branching was observed between the visually counted values and the values extracted using the developed method, with R²=0.97.  Using the method, we found that the interspecific advantages for maize mainly occurred within 5 cm from the root base in the nodal roots of the 5th–7th nodes, and that the obvious inhibition of soybean was mostly reflected within 20 cm from the root base.  Our study provides a novel approach with high-throughput and high-accuracy for field research on root morphology and branching features.  It could be applied to the 3D reconstruction of field-grown root system architecture to improve the inputs to data-driven models (e.g., OpenSimRoot) that simulate root growth, solute transport and water uptake.