发表机构
Osnabrück University; Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB); German Research Center for Artificial Intelligence (DFKI)（奥斯纳布吕克大学; 莱布尼茨农业工程与生物经济研究所（ATB）; 德国人工智能研究中心（DFKI））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对农业预测任务中训练数据受限影响机器学习性能的问题，提出任务条件合成数据生成算法TCSDG，结合贝叶斯网络生成器与表格基础模型，经实验验证其能有效提升性能，优于基准算法，提供实用框架。
AI 中文摘要
机器学习算法已广泛用于估计农业变量，但训练数据的数量和质量会影响其性能，受限于参考数据。合成数据生成（SDG）可解决此问题。本研究基于师生知识转移和表格数据上下文学习，提出任务条件SDG（TCSDG）算法，将贝叶斯网络生成器与基于Transformer的表格基础模型配对。在作物产量预测和作物类型分类任务中评估该算法，并与六种基准SDG算法比较。结果显示，TCSDG生成的合成数据在多数实验中提升了机器学习性能，且显著优于基准算法，为农业预测提供了实用框架。
英文摘要
Machine Learning (ML) algorithms have been widely used to estimate agricultural variables across diverse contexts. However, because the quantity and quality of training data strongly influence performance of ML algorithms, their use can be constrained by limited or incomplete reference data. Synthetic Data Generation (SDG) offers a practical approach to address this issue by producing artificial but realistic samples that preserve key characteristics of the original data. Building on teacher-student knowledge transfer and in-context learning for tabular data, this study proposes a Task-Conditioned SDG (TCSDG) algorithm that pairs a Bayesian Network generator with a transformer-based tabular foundation model (TabICL). The proposed algorithm was evaluated on two agricultural prediction tasks: crop yield prediction and crop type classification. Six benchmark SDG algorithms were also utilized to compare their performance with that of TCSDG. Across twelve study sites, two training-data fractions, four multiplication ratios, and three predictive ML algorithms, augmenting the original data with TCSDG-generated synthetic data improved ML performance in 89% of the crop type classification experiments and 74% of the crop yield prediction experiments. TCSDG also substantially outperformed benchmark SDG algorithms and was the only method to consistently improve ML performance across both tasks at the aggregate level. The study demonstrates that carefully designed and processed synthetic data can improve ML performance in precision-agriculture applications. TCSDG offers a practical and extensible framework for generating synthetic data that supports downstream ML agricultural prediction. The full implementation of TCSDG is publicly available as open source at https://github.com/HamidEbrahimy/TCSDG.
