In contemporary precision agriculture, forecasting plant growth is essential for crop health monitoring, yield projection, and resource optimization. Deep learning has become a potent tool for simulating intricate biological processes, like plant growth, as artificial intelligence has advanced. With an emphasis on the usage of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) models, and hybrid architectures, this survey examines the state of deep learning approaches used to forecast plant development. In order to train prediction models, the study examines a variety of datasets, feature extraction strategies, environmental factors, and imaging methodologies. It also discusses important issues including data scarcity and model interpretability, evaluates the advantages and disadvantages of various strategies, and suggests possible directions for further study. The purpose of this survey is to give scholars and professionals a thorough grasp of how deep learning is changing agricultural forecasting and decision-making.