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.
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.
In March 2024, Plant Phenomics published a research article titled « Improved Transformer for Time Series Senescence Root Recognition. » This study focuses on utilizing the RhizoPot system and exploring root segmentation models to enhance root senescence recognition, aiming to fill the gap in efficient, accurate root analysis for better plant health monitoring.
