Abstract: Abstract: Due to roots are hidden underground and require considerable effort to characterize, research on plant roots lags behind that of the aboveground organs. This study presented an image-based high-throughput root phenotyping system, which integrated a reliable and simple image acquisition unit and an automatic image analysis software. The hardware platform utilized a blue background cloth to simplify root image content, the tripod was placed in front of the background cloth with a disk marker pasted on its top, root sample bundles with the wishbone of the tripod using a fishing line. A camera (Canon EOS 5D Mark III) with a 28 mm focal length lens faced the background cloth and was focused on the root crown, with aperture-priority model and other settings remain default. Coupled with the optimized image acquisition using the hardware platform, segmentation of the root images from the background required only the Support Vector Machine (SVM) based on the pixel classifying method with a 15-dimensional eigenvector. The contour of a disk marker was roughly spherical, a circularity scoring evaluation function was used to distinguish other compositions according to thresholding of 0.85. Along the stem of the root image, when the width increased up to 1.5 times, the assumed root original point was found. Contaminated data was generated during the sampling process due to root vulnerability nature; a redundant root pruning method was introduced by circular covering beyond 90% of the root area. The root angle of branching was defined as the sum of left and right angles which was calculated by circular covering beyond 90% root area with a 10 cm radius. The convex hull of the root was a polygon, binarization of the root image was the connected domain, the exclusive-or produced gaps within the root region defined as "inner gap" or between the convex and connected domain defined as "gap". Root uniformity assessment was achieved by evaluating the distance between the center of gravity and the center of the min-enclosure circle of root image pixels. To explain the color difference of roots, introduced 6 channels of Hue Saturation Value (HSV) and Lab color spaces, and a Dark Green Color Index (DGCI) accounted for green degree difference between root images. The software platform was designed to quickly analyze the images acquired using the hardware platform and was to create a simple-to-use and robust program that batch processed a list containing root crown images and output a data file with the measures for each sample in a form convenient for data analysis. A total of 29 phenotyping indices were extracted from each input image, which was stored in a Comma-Separated Values (CSV) text file. Segmented images as well as processed images on which visual depictions of the extracted features were drawn on the intermediate image. Multithread image processing and final phenotyping indices results were visualizations in the computational processing display area. Finally, 29 phenotyping indices of 135 maize inbred lines were available, the preliminary statistical was conducted to describe extreme value distribution. Phenotyping indices measuring accuracy were improved by root angle of branching correlative analysis method between manual measurement and automatic method in this platform, the coefficient of determination between two methods hit 0.85. An unsupervised clustering (K-means) method classified 135 maize inbred lines into 3 classifications, by the aid of a profile chart and root classed results could conclude that phenotyping indices in morphological characters, color features, and spatial arrangement played an important role in the identification of different types. The platform demonstrated in this study that made the high-throughput maize root phenotyping come to be true, with the help of further researches such as selecting a high nitrogen-use efficiency plant according to root phenotyping indices could be done.