The Rhizotrons method is an important means of detecting dynamic growth and development phenotypes of plant roots. However, the segmentation of root images is a critical obstacle restricting further development of this method. At present, researchers mostly use direct manual drawings or software-assisted manual drawings to segment root systems for analysis. Root systems can be segmented from root images obtained by the Rhizotrons method, and then, root system lengths and diameters can be obtained with software. This type of image segmentation method is extremely inefficient and very prone to human error. Here, we investigate the effectiveness of an automated image segmentation method based on the DeepLabv3+ convolutional neural network (CNN) architecture to streamline such measurements. We have improved the upsampling portion of the DeepLabv3+ network and validated it using in situ images of cotton roots obtained with a micro root window root system monitoring system. Segmentation performance of the proposed method utilizing WinRHIZO Tron MF analysis was assessed using these images. After 80 epochs of training, the final verification set F1-score, recall, and precision were 0.9773, 0.9847, and 0.9702, respectively. The Spearman rank correlation between the manually obtained Rhizotrons manual segmentation root length and automated root length was 0.9667 (p < 10^–8), with r² = 0.9449. Based on the comparison of our segmentation results with those of traditional manual and U-net segmentation methods, this novel method can more accurately segment root systems in complex soil environments. Thus, using the improved DeepLabv3+ to segment root systems based on micro-root images is an effective method for accurately and quickly segmenting root systems in a homogeneous soil environment and has clear advantages over traditional manual segmentation.

根瘤菌方法是检测植物根系动态生长和发育表型的重要手段。然而，根图像的分割是限制该方法进一步发展的关键障碍。目前，研究人员大多使用直接的手动图或软件辅助的手动图来分割根系统以进行分析。根系可以从通过Rhizotrons方法获得的根图像中分割出来，然后可以使用软件获得根系的长度和直径。这种类型的图像分割方法效率极低，极易出现人为错误。在这里，我们研究了基于DeepLabv3 +卷积神经网络（CNN）架构的自动图像分割方法来简化此类测量的有效性。原位用微根窗根系监测系统获得的棉花根系图像。使用这些图像评估了使用WinRHIZO Tron MF分析的拟议方法的分割性能。经过80次训练后，最终的验证集F1得分，召回率和精度分别为0.9773、0.9847和0.9702。手动获得的Rhizotrons手动分割根长度和自动根长度之间的Spearman等级相关性为0.9667（p<10 ^–8），其中[R² = 0.9449。在将我们的分割结果与传统的手动和U-net分割方法进行比较的基础上，这种新颖的方法可以更精确地分割复杂土壤环境中的根系。因此，使用改进的DeepLabv3 +基于微根图像分割根系是在均匀土壤环境中准确，快速分割根系的有效方法，与传统的手动分割相比具有明显的优势。