渐变增强机器学习模型来预测爱荷华州玉米中的 aflatoxins.
Emily H Branstad-Spates1, Lina Castano-Duque2, Gretchen A Mosher1
1Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, United States.
Frontiers in microbiology
|September 18, 2023
概括
玉米中阿弗拉托克辛 (AFL) 污染可以使用机器学习模型预测,该模型包含天气,卫星和土壤数据. 该模型有助于积极的危险管理,以获得更安全的食品和料供应.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 玉米中阿弗拉托克辛 (AFL) 的污染,由于其有毒和致癌性质,对健康构成重大风险.
- 确保美国的食品和料安全,需要对这一至关重要的商品制定有效的AFL缓解策略.
研究的目的:
- 开发和评估一个以爱荷华州为中心的预测模型,用于玉米中AFL污染.
- 用历史数据,气象,卫星和土壤特性进行AFL风险评估.
主要方法:
- 渐变增强机器 (GBM) 学习被用于AFL预测.
- 两个AFL风险值 (20-ppb和5-ppb) 用90%-10%的培训与测试比率和独立验证进行了评估.
- 应用特征工程来识别关键的预测变量.
主要成果:
- GBM模型实现了高整体精度 (20ppb的96.77%,5ppb的90.32%),但对于检测高污染事件的灵敏度较低.
- 8月份来自卫星的植被指数显著改善了季末污染预测.
- 五月和七月的亚拉托克辛风险指数 (ARI),度和土壤和液压导电性 (Ksat) 被确定为影响因素.
结论:
- 预测性AFL模型是实用的谷物处理,使预防性而不是反应性的缓解.
- 确定年度AFL风险预测因素对于成本有效的危险管理和玉米作物的最佳利用至关重要.
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