Non-Linear Model-Based Sequential Decision-Making in Agriculture
The paper addresses agricultural decision-making by using nonlinear model-based bandit algorithms to manage fertilizer application under uncertainty. It embeds specific agronomic response models (like Mitscherlich and Michaelis-Menten) directly into the learning process. This approach provides interpretable recommendations and improves sample efficiency when making sequential choices about input levels.