Drought is a key constraint for present and future crop production and selecting the right plant water uptake strategies is essential to set robust crop management practices. In this context, the root system plays a central role in how plants access water. Functional root traits such as root hydraulic conductance are difficult to measure directly whereas structural traits are easier to phenotype and can be translated into function through modeling. However, field root phenotyping remains challenging. Most approaches are slow and provide either structural or anatomical traits but not as an integrated set. We develop an optimized phenotyping pipeline to measure structural and anatomical root parameters from field samples that could then serve directly as computational model input. The workflow starts with wheat shovelomics samples and targets root number, root diameter, interlateral root distance and cortex/stele size. Roots were counted manually as our imaging step works with individual roots. We designed a compact backlight imaging box that enables fast and standardized capture of high resolution images. We tested subsampling to reduce processing time while keeping the representativeness of the whole crown. Images were segmented automatically using RootPainter and anatomical traits were extracted using a novel dedicated tool. Structural data were benchmarked against SmartRoot tracing. Anatomical outputs were validated against cross-sections images acquired using Rapid Anatomical Tool (RAT). In conclusion, we provide an integrated and low-cost pipeline that merges fast imaging with automated extraction of structural and anatomical traits.