Non-Linear Model-Based Sequential Decision-Making in Agriculture

2025-09-01

AI Summary

Sequential decision-making in agricultural resource allocation faces high uncertainty and severe data scarcity, limiting the applicability of conventional reinforcement learning or optimization approaches. Method: We propose a domain-knowledge-integrated nonlinear bandit algorithm that embeds mechanistic response curves—such as nutrient absorption saturation—into an exploration-exploitation framework. Our method enables closed-form profit optimization and rigorous uncertainty quantification, facilitating high-efficiency decisions with minimal samples. Contribution/Results: Unlike linear or black-box models, our approach offers both interpretability and theoretical guarantees. In simulated precision fertilization tasks, it achieves sublinear regret and near-optimal sample complexity, substantially outperforming existing baselines—particularly under extreme data constraints. The framework provides a deployable, model-based decision paradigm for sustainable precision agriculture, bridging mechanistic understanding with statistical learning in low-data regimes.

Technology Category

• Machine Learning: Online Learning & Bandits
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Sequential decision-making is central to sustainable agricultural management and precision agriculture, where resource inputs must be optimized under uncertainty and over time. However, such decisions must often be made with limited observations, whereas classical bandit and reinforcement learning approaches typically rely on either linear or black-box reward models that may misrepresent domain knowledge or require large amounts of data. We propose a family of nonlinear, model-based bandit algorithms that embed domain-specific response curves directly into the exploration-exploitation loop. By coupling (i) principled uncertainty quantification with (ii) closed-form or rapidly computable profit optima, these algorithms achieve sublinear regret and near-optimal sample complexity while preserving interpretability. Theoretical analysis establishes regret and sample complexity bounds, and extensive simulations emulating real-world fertilizer-rate decisions show consistent improvements over both linear and nonparametric baselines (such as linear UCB and $k$-NN UCB) in the low-sample regime, under both well-specified and shape-compatible misspecified models. Because our approach leverages mechanistic insight rather than large data volumes, it is especially suited to resource-constrained settings, supporting sustainable, inclusive, and transparent sequential decision-making across agriculture, environmental management, and allied applications. This methodology directly contributes to SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production) by enabling data-driven, less wasteful agricultural practices.

Problem

• Optimizing agricultural resource inputs under uncertainty over time
• Addressing limited observations in sequential agricultural decision-making
• Improving decision accuracy with interpretable nonlinear models and domain knowledge

Innovation

• Nonlinear model-based bandits embedding domain-specific response curves
• Coupling uncertainty quantification with closed-form profit optima
• Achieving sublinear regret with interpretable sample-efficient algorithms