In Situ Root Dataset Expansion Strategy Based on an Improved CycleGAN Generator.

ABSTRACT: The root system plays a vital role in plants' ability to absorb water and nutrients. In situ root research offers an intuitive approach to exploring root phenotypes and their dynamics. Deep-learning-based root segmentation methods have gained popularity, but they require large labeled datasets for training. This paper presents an expansion method for in situ root datasets using an improved CycleGAN generator. In addition, spatial-coordinate-based target background separation method is proposed, which solves the issue of background pixel variations caused by generator errors. Compared to traditional threshold segmentation methods, this approach demonstrates superior speed, accuracy, and stability. Moreover, through time-division soil image acquisition, diverse culture medium can be replaced

SUBMITTER: Yu Q 

PROVIDER: S-EPMC11020132 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature