更安全和更智能:利用解释导向建模和疾病和环境数据的数据合并来预测植物疾病风险

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Kaique S Alves¹, Denis A Shah², Hellen R Dillard³

¹Universidade Federal de Viçosa, Departamento de Fitopatologia, Av. Peter Henry Rolfs, s/n, vicosa, Vicosa, minas gerais, Brazil, 36570900; kaiquedsalves@gmail.com.

|August 2, 2025

概括

复杂的机器学习 (ML) 模型可能会过度适应小型植物疾病数据集. 使用 ML 来获取洞察力引导了一个更简单的后勤回归模型,改进了对豆中的白菌的预测.

科学领域:

植物病理学植物病理学
农业科学农业科学
计算流行病学计算流行病学

背景情况:

机器学习 (ML) 算法通常需要大量的数据集,这对植物疾病流行病学提出了挑战,因为数据可能有限.
当将复杂的ML模型应用于较小的数据集时,过度装配是一个重要的风险,可能导致不可靠的预测.

研究的目的:

用有限的数据展示一种以解释为导向的建模方法,用于使用有限的数据预测植物疾病.
在流行病学研究中克服ML过度匹配的挑战,通过使用ML来产生洞察力.

主要方法:

案例研究:白色菌 (Sclerotinia sclerotiorum) 在豆 (Phaseolus vulgaris) 中.
数据整合:将现场观测与土壤和环境数据 (ERA5-Land) 合并.
ML应用:使用功能数据分析和随机森林 (RF) 来产生洞察力,然后用SHapley添加式解释 (SHAP) 来解释.
模型开发:在RF洞察的指导下,开发了一个更简单的物流回归模型.

主要成果:

复杂的射频模型表现出明显的高性能,但由于过度装配和校准不佳而受到影响.
对射频模型的解释确定了白风险的关键预测因素.
更简单的逻辑回归模型,以ML洞察为指导,显示出更好的校准和可接受的歧视 (C-统计 = 0.77).

结论:

复杂的ML模型对于从较小的流行病学数据集中产生见解可能是有价值的.
一种以解释为导向的方法可以导致更简单,更强大,更好地校准的预测模型.
这种策略适用于在资源有限的情况下做出实际的疾病管理决策.

关键词:

数据科学数据科学数据科学流行病学流行病学建模模型建模模型建模模型

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