Crop Simulation

Crop simulation uses computational models to predict crop growth and yield under various conditions, aiming to optimize agricultural practices and improve food security. Current research heavily utilizes machine learning, particularly reinforcement learning and deep neural networks, often integrated with high-fidelity crop simulators like DSSAT, to develop intelligent decision-support systems for tasks such as irrigation scheduling and fertilizer application. These advancements enable more precise and sustainable management strategies, leading to increased yields, reduced environmental impact, and improved economic profitability for farmers. The integration of remote sensing data further enhances model accuracy and applicability across larger spatial scales.