 Deep learning, DL, approaches have shown great potential in plant growth monitoring due to their ability to accurately classify images and extract relevant features. However, there are still many challenges that must be addressed before these approaches can be widely adopted. These include the need for large amounts of annotated data, the high computational requirements of training models, and the difficulty of extracting both spatial and temporal features simultaneously. Despite these challenges, DL approaches have made significant progress in the field of plant growth monitoring and will continue to do so in the future. This article was authored by Yin Sayun Tong, Tu Hong Li, and Kin Sam Yen.