Roots play a vital role in plant health, adapting to environmental changes and indicating crop growth. However, studying root senescence is challenging due to difficulties in obtaining clear in situ root images. Traditional methods are limited, and while in situ cultivation and advanced imaging techniques offer some solutions, they face issues such as high costs and low image quality. Recent advances in deep learning, particularly semantic segmentation models like SegNet and UNet, have improved root identification but still require further optimization.
Improving root senescence recognition with a new semantic segmentation model
