Grass Growth

Research on grass growth encompasses diverse applications, from optimizing agricultural practices to enabling autonomous robotic navigation in complex environments. Current studies leverage machine learning, particularly deep learning models and graph neural networks, to analyze diverse data sources including images, LiDAR point clouds, and climate data for improved prediction and decision-making. These advancements aim to enhance efficiency in areas such as nitrogen fertilization, biomass estimation, and robotic monitoring of grassland ecosystems, ultimately contributing to more sustainable and data-driven approaches in agriculture and environmental science.