Agent-based post-hoc correction of agricultural yield forecasts

Accurate crop yield forecasting in commercial soft fruit production is constrained by the data available in typical commercial farm records, which lack the sensor networks, satellite imagery, and high-resolution meteorological inputs that most state-of-the-art approaches assume. Even when provided with recent historical observations, existing forecasting models exhibit systematic, season-specific errors that growers can readily identify but that the models have no mechanism to self-correct. This work proposes a structured LLM agent framework that performs post-hoc correction of existing model predictions without requiring additional data or retraining, encoding agricultural domain knowledge across a five-tool pipeline covering seasonal phase detection, bias learning, range validation, cross-plot similarity search, and correction synthesis. The framework operates as a model-agnostic layer on top of any base forecaster, with each correction traceable to an explicit reasoning step. Evaluated on a proprietary strawberry yield dataset and a public USDA corn harvest dataset, agent refinement of XGBoost reduced MAE by 22% and MASE by 54% on strawberry, with consistent improvements across Moirai2 (MAE −25%, MASE −22%) and Random Forest (MAE −31%, MASE −61%) baselines. Using Llama 3.1 8B as the agent produced the strongest LLM-based corrections across all configurations, the Deterministic baseline was competitive with or outperformed Llama 3.1 8B on XGBoost and Random Forest, suggesting that domain-tool correction alone recovers much of the available gain. LLaVA 13B showed inconsistent gains, highlighting sensitivity to the choice of refinement model.