Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts

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Beddows, Matthew, Durrant, Aiden ORCID: https://orcid.org/0000-0002-8375-4523 and Leontidis, Georgios (2026) Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts. Frontiers in Artificial Intelligence. ISSN 2624-8212 (In Press)

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Abstract

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.

Item Type: Article
Additional Information: Data Availability: The strawberry yield dataset was provided under a commercial agreement and cannot be made publicly available. The corn dataset is publicly available from the USDA NASS QuickStats database at https://quickstats.nass.usda.gov/ and can be reproduced using the queries described in Section 3.
Uncontrolled Keywords: llm agents,time series forecasting,agricultural yield prediction,xg boost,moirai2
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Data Science and AI
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Depositing User: LivePure Connector
Date Deposited: 16 Sep 2026 11:51
Last Modified: 16 Sep 2026 11:55
URI: https://ueaeprints.uea.ac.uk/id/eprint/104563
DOI:

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