Crop Model

Crop models are computational tools used to simulate plant growth and predict crop yields, aiming to optimize agricultural practices and improve food security. Current research emphasizes integrating process-based models with machine learning techniques, such as neural networks (including LSTMs and convolutional nets) and ensemble Kalman filters, to enhance prediction accuracy and address challenges like model calibration and data scarcity. This work is driven by the need for more precise yield forecasts and improved decision-making regarding fertilizer application, irrigation, and cultivar selection, ultimately contributing to more efficient and sustainable agriculture.