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. We propose a structured LLM agent framework that performs post-hoc correction of existing model predictions, encoding agricultural domain knowledge across tools for phase detection, bias learning, and range validation. Evaluated on a proprietary strawberry yield dataset and a public USDA corn harvest dataset, agent refinement of XGBoost reduced MAE by 20% and MASE by 56% on strawberry, with consistent improvements across Moirai2 (MAE 24%, MASE 22%) and Random Forest (MAE 28%, MASE 66%) baselines. Using Llama 3.1 8B as the agent produced the strongest corrections across all configurations; LLaVA 13B showed inconsistent gains, highlighting sensitivity to the choice of refinement model.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Sparsity, Regularization and Causality in Agricultural Yield: The Case of Paddy Rice in Peru
This study introduces a novel approach that integrates agricultural census data with remotely sensed time series to develop precise predictive models for paddy rice yield across various regions of Peru. By utilizing spar…
ManagementTime SeriesDesigning probabilistic AI monsoon forecasts to inform agricultural decision-making
Hundreds of millions of farmers make high-stakes decisions under uncertainty about future weather. Forecasts can inform these decisions, but available choices and their risks and benefits vary between farmers. We introdu…
Bayesian InferenceImputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic
Streamflow observation data is vital for flood monitoring, agricultural, and settlement planning. However, such streamflow data are commonly plagued with missing observations due to various causes such as harsh environme…
Decision MakingImputationregressionTime Series+1Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecasts
Ensemble forecasts from numerical weather prediction models show systematic errors that require correction via post-processing. While there has been substantial progress in flexible neural network-based post-processing m…
Graph Neural NetworkDemocratising Agricultural Commodity Price Forecasting: The AGRICAF Approach
Ensuring food security is a critical global challenge, particularly for low-income countries where food prices impact the access to nutritious food. The volatility of global agricultural commodity (AC) prices exacerbates…