Wang X, Borjesson T, Wetterlind J, van der Fels-Klerx H J
Business Economics Group, Wageningen University, Hollandseweg 1, Wageningen, the Netherlands.
Wageningen Food Safety Research, Akkermaalsbos 2, Wageningen, the Netherlands.
NPJ Sci Food. 2024 Oct 4;8(1):75. doi: 10.1038/s41538-024-00310-w.
Weather conditions and agronomical factors are known to affect Fusarium spp. growth and ultimately deoxynivalenol (DON) contamination in oat. This study aimed to develop predictive models for the contamination of spring oat at harvest with DON on a regional basis in Sweden using machine-learning algorithms. Three models were developed as regional risk-assessment tools for farmers, crop collectors, and food safety inspectors, respectively. Data included: weather data from different oat growing periods, agronomical data, site-specific data, and DON contamination data from the previous year. Results showed that: (1) RF models were able to predict DON contamination at harvest with a total classification accuracy of minimal 0.72; (2) good predictions could already be made in June; (3) rainfall, relative humidity, and wind speed in different oat growing stages, followed by crop variety and elevation were the most important features for predicting DON contamination in spring oats at harvest.
众所周知，天气条件和农艺因素会影响镰刀菌的生长，并最终影响燕麦中脱氧雪腐镰刀菌烯醇（DON）的污染情况。本研究旨在利用机器学习算法，在瑞典区域层面上建立春季燕麦收获时DON污染的预测模型。分别开发了三种模型，作为面向农民、作物收集者和食品安全检查员的区域风险评估工具。数据包括：不同燕麦生长时期的气象数据、农艺数据、特定地点数据以及上一年的DON污染数据。结果表明：（1）随机森林（RF）模型能够预测收获时的DON污染，总分类准确率最低为0.72；（2）6月份就已经能够做出良好的预测；（3）不同燕麦生长阶段的降雨量、相对湿度和风速，其次是作物品种和海拔高度，是预测春季燕麦收获时DON污染的最重要特征。
