A new method for expanding in situ root datasets using CycleGAN

The root system is crucial for plants to absorb water and nutrients, with in situ root research providing insights into root phenotypes and dynamics. While deep-learning-based root segmentation methods have advanced the analysis of root systems, they require extensive manually labeled datasets, which are labor-intensive and time-consuming to produce. Current methods of in situ root observation vary in their effectiveness.