High-Throughput in situ Root Image Segmentation Based on the Improved DeepLabv3+ Method.

ABSTRACT: 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

SUBMITTER: Shen C 

PROVIDER: S-EPMC7604297 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature