用深度学习预测爱尔兰燕麦霉菌毒素污染，提升食品安全预警能力
Predicting Mycotoxin Contamination in Irish Oats Using Deep and Transfer Learning
- 采用迁移学习模型，结合气象与农艺数据进行多毒素联合预测
- TabPFN表现最优，90天预收获期天气模式是关键影响因素
- 适合农业安全监测与粮食质量管控人员参考
霉菌毒素污染严重威胁谷物品质、食品安全和农业生产效率。准确预测霉菌毒素水平可支持早期干预并减少经济损失。本研究探讨神经网络与迁移学习模型在爱尔兰燕麦作物中预测霉菌毒素污染的多响应预测任务中的应用。数据集包含在爱尔兰采集的燕麦样本，涵盖环境、农艺和地理预测因子。评估了五种建模方法：基础多层感知机（MLP）、带预训练的MLP，以及三种迁移学习模型（TabPFN、TabNet、FT-Transformer）。使用回归（RMSE、$R^2$）和分类（AUC、F1）指标评估模型性能，结果按毒素类型及平均值报告。此外，通过置换变量重要性分析识别两类任务中的关键预测因子。结果显示，迁移学习模型TabPFN整体表现最佳，其次为基准MLP。变量重要性分析表明，收获前90天的天气历史模式是最重要预测因子，种子含水率亦具显著影响。
原文摘要 · Abstract (English)
Mycotoxin contamination poses a significant risk to cereal crop quality, food safety, and agricultural productivity. Accurate prediction of mycotoxin levels can support early intervention strategies and reduce economic losses. This study investigates the use of neural networks and transfer learning models to predict mycotoxin contamination in Irish oat crops as a multi-response prediction task. Our dataset comprises oat samples collected in Ireland, containing a mix of environmental, agronomic, and geographical predictors. Five modelling approaches were evaluated: a baseline multilayer perceptron (MLP), an MLP with pre-training, and three transfer learning models; TabPFN, TabNet, and FT-Transformer. Model performance was evaluated using regression (RMSE, $R^2$) and classification (AUC, F1) metrics, with results reported per toxin and on average. Additionally, permutation-based variable importance analysis was conducted to identify the most influential predictors across both prediction tasks. The transfer learning approach TabPFN provided the overall best performance, followed by the baseline MLP. Our variable importance analysis revealed that weather history patterns in the 90-day pre-harvest period were the most important predictors, alongside seed moisture content.
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