Abstract: A root is one of the most important vegetative organs of plants. It is of great significance to explore the growth status of root under different water stress in modern agriculture. However, the existing observation of plant root are cumbersome, laborious and time-consuming. It is the high demand to meet the requirements of precision irrigation and water-saving under field conditions. In this study, a pipeline robot system was developed for the synchronous, in-situ and the high-throughput monitoring of plant root and root soil water using STM32. The system consisted of pipeline robot, data base station and PVC transparent pipeline. The pipeline robot system was embedded in the soil, where the macro camera and soil moisture sensor were carried by the robot. The root images were captured to obtain the soil moisture data while the robot cruising. Meanwhile, the robot shared the functions of autonomous timing cruise, wireless communication using command data and active return after encountering obstacles. The distorted correction, registered on plane, identified and segmented images were obtained for the parameters of the root area, length and density of the plants in the direction of the pipeline. The results of laboratory test show that: 1) The pipeline robot was clearly captured the root images, indicating the excellent performance on distortion correction and plane registration. The true length and area corresponding to a single pixel were 44 μm and 0.002 mm², respectively, whereas, the shooting range of a single root image was 14.17×10.60 mm. The characteristic information of root was obtained using MATLAB, where the images taken by the automatic cruise of pipeline robot. The image processing operations included the image distortion correction, image preprocessing, root region recognition and segmentation, and root feature extraction. Compared with the root characteristic parameters measured by the excavation , the relative errors of the root image processing program were 12.29%, 3.40% and 12.50%, respectively; 2) There was an excellent linear relationship between the output voltage of the soil moisture sensor that carried by the pipeline robot and the soil volumetric moisture content, where the coefficient of determination was 0.990; 3) The pipeline robot presented a high accuracy of autonomous cruise positioning, with a mean relative error of 1.47%. The field experiment show that: 1) The pipeline robot system was captured the plant root images with high-throughput in the field environment. Root growth dynamics was obtained to further extract the parameters of root length, area, average diameter and density from the images; 2) The pipeline robot was accurately monitor the soil moisture in the root zone, where the mean relative error of the measured was 2.23%, compared with the drying measurement; 3) Once the system was initially fully charged, the pipeline robot system operated independently for no less than 7 days, where the maximum cruise monitoring distance was about 48m. The pipeline robot system can be expected to realize in-situ and high-throughput measurement of plant root and soil moisture in the root zone under field environment. The growth of root can be extracted after image recognition and segmentation. The finding can also provide the technical support to monitor the growth status of root in water-saving irrigation.