Deep learning has become a powerful tool for enhancing smart greenhouse management. This paper presents a comprehensive survey of deep learning applications in smart greenhouses, focusing on both crop-centric and environment-centric use cases. While significant effectiveness has been achieved, challenges such as limited datasets and computational constraints in greenhouse management are also discussed. To address these issues, we propose a novel IoT architecture based on federated learning, aimed at improving data privacy, resource reuse efficiency, and model adaptability across diverse greenhouse environments. The paper concludes by outlining future research directions, emphasizing the need for standardized datasets, advanced sensor integration, and explainable AI techniques in greenhouse management.