Abstract: Abstract: This study aims to improve the detection precision of plant root phenotyping in the images, particularly with broken roots. An algorithm of radial growing repair was proposed to apply to the evaluation of maize seed's resistance to damage. A soil culture experiment was also conducted on the Pukou campus of Nanjing Agricultural University in China every month. After that, four varieties of corn seeds were selected: Yufeng 303 (ordinary control group), Zhengdan 958 (drought resistance group), Liyu 16 (water resistance group), and Zhengdan 958 (salt resistance group). Four kinds of resistant corn seeds were planted in trough-shaped and flat containers for 14d, including ordinary, drought-resistant, water-resistant, and salt-resistant corn seeds. Subsequently, the root system was taken out to rinse the residual soil with tap water, and then placed on a solid-color background plate to level out. Prior to image acquisition, the root length and diameter were measured by a ruler, and the number of main and lateral roots was counted to record. The specific procedure of image processing was as follows. Firstly, a series of operations was used to preprocess the collected images for the extraction of root systems from complex backgrounds, such as adaptive contrast enhancement, histogram grayscale searching, and pepper-salt denoising. As such, the discrimination of root images was improved to remove the noise during image acquisition, such as reflections and water stains. Secondly, the tips of main roots in maize images were detected by training the YOLO-V3 neural network. Finally, the radial growth repair algorithm was presented, including the direction discrimination of main roots in bifurcation points, and adaptive repair in end points. These strategies greatly contributed to extracting phenotypic parameters from main and lateral roots. Maize root datasets were also selected to evaluate the practicality and accuracy of radial growth repair. The results demonstrated that the phenotypic accuracy of repaired main roots lengths and diameter increased from 83.6% and 84.4% to 97.4% and 94.8%, respectively, compared with that processed by region growth algorithm. The phenotypic parameters extracted by radial growth repair algorithm was more precise than that extracted by region growth algorithm, which indicated that radial growth repair algorithm was suitable for extraction of maize root system parameters in different stress environments. The accuracy of radial growth was improved more obviously in the salty environments and drought environments. The results in this study demonstrated that the proposed radial growth repair algorithm could improve the accuracy of root image phenotype detection and could be efficient for maize resistance evaluation, which provided reference for the rapid extraction of root system phenotype.