Abstract: Minirhizotron technology promotes the study of root phenotype, but the root length and diameter still require human eye recognition to draw the track, which consumes a lot of manpower and time.This study introduced U-Net semantic segmentation technology into plant root image recognition, and developed iRoot-V02 software based on machine learning to solve this problem.The iRoot-V02 software was used to identify the root length, diameter, projection area, and root tip number from plant root imaging images obtained by Minirhizotron technology.The results show that the average speed of iRoot-V02 software for processing 600 dpi images in batches is 26.6 seconds per picture.The skeleton information and total length of roots are essentially consistent with the human eye recognition results.According to the diameter of each 0.1 mm as a level, the correlation coefficient between the obtained root length of different diameters and the eye recognition results is larger than 0.76, reflecting that the software accurately captures the changes of root lengths of different diameters in the vigorous growth period of maize.In addition, the analysis of the parameters of root images with 300 dpi and 600 dpi resolutions shows that their results are highly correlated.Therefore, the relationship equation between low-and high-resolution root parameters can be established to confirm more accurate root parameters with the low-resolution root image and to reduce the workload.Root growth information obtained by using iRoot-V02 software is similar to that of human eye recognition.On a whole, compared with human eye recognition, iRoot-V02 has great advantages in mass root image intelligent recognition, automation and fast target detection.