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The root system is essential not only for nutrient uptake and anchoring the plant but also for shaping crop development and responses to environmental stress. Rhizobox setups permit in situ, non-invasive root observation in soil, representing an economical imaging strategy. Nevertheless, soil’s non-transparent nature frequently produces discontinuous root images with intermittent gaps, diminishing the precision of derived root phenotypic metrics. We propose a root inpainting methodology grounded in a Generative Adversarial Network (GAN) framework. Furthermore, a hybrid root inpainting dataset (HRID) was assembled, containing 1206 cotton root images with genuine gaps and 7716 rice root images with synthetically introduced gaps. Compared with computationally simulated root depictions, our dataset provides authentic root system architecture (RSA) and textural characteristics. Rather than cropping during training, our technique uses downsampled versions of the full image to convey the complete root morphology. Training the model uses binary cross-entropy loss to differentiate between root and background pixels.Additionally, Dice loss is incorporated to counteract the effects of uneven class representation. Skip connections typical of U-Net are removed, and an edge attention module (EAM) is introduced to retain more granular structural details. Compared with competing approaches, our technique substantially increases recall from 17.35% to 35.75% on a cotton root test collection of 122 images, indicating marked gains in inpainting fidelity. For root area, root length, convex hull area, and root depth, the trait error reduction rates (TERRs) reach 76.07%, 68.63%, 48.64%, and 88.28%, respectively, delivering significantly more accurate root phenotyping outcomes. Both the EU-GAN source code and the 8922 annotated images are openly accessible, offering potential reuse in other AI-driven investigations. This work provides a resilient solution for root phenotyping, thereby accelerating breeding workflows and expanding knowledge of root system behavior.