Plant Growth Simulation

Plant growth simulation aims to create realistic digital representations of plant growth, enabling researchers to study plant development under various conditions and optimize agricultural practices. Current research focuses on developing sophisticated models, incorporating machine learning techniques like convolutional neural networks (CNNs) and transformers, along with physics-based simulations and data from high-resolution sensors (e.g., multispectral imaging, LiDAR) to improve accuracy and realism. These advancements are improving precision agriculture by enabling more efficient weed detection, optimized resource allocation (e.g., water), and improved crop yield predictions, ultimately contributing to sustainable food production.