用智能代理修正作物产量预测，提升商业果园精度。
Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts
- 设计基于大模型的智能代理，事后修正已有预测结果。
- 在草莓数据上使平均绝对误差降低20%，马氏误差降低56%。
- 适合缺乏高精度数据的农业场景，尤其适用小规模农场。
商业软果生产中的精准作物产量预测受限于常规农场记录中缺乏传感器网络、卫星影像和高分辨率气象数据，而当前先进方法多依赖这些条件。本文提出一种结构化的大型语言模型代理框架，用于对现有模型预测进行事后修正，融合农业领域知识，实现生长阶段识别、偏差学习与预测范围验证。在自有草莓产量数据集和公开的美国农业部玉米收获数据集上评估显示，对XGBoost的代理修正使草莓的平均绝对误差（MAE）降低20%，马氏误差（MASE）降低56%；在Moirai2（MAE降低24%，MASE降低22%）和随机森林（MAE降低28%，MASE降低66%）等基线模型上也均取得一致改进。使用Llama 3.1 8B作为代理时效果最佳；而LLaVA 13B表现不一致，表明修正模型选择对结果敏感。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。