Soil-borne plant diseases are increasingly causing devastating losses in agricultural production. The development of a more refined model for disease prediction can aid in reducing crop losses through the use of preventative control measures or soil fallowing for a planting season. The emergence of high-throughput DNA sequencing technology has provided unprecedented insight into the microbial composition of diseased versus healthy soils. However, a single independent case study rarely yields a general conclusion predictive of the disease in a particular soil. Here, we attempt to account for the differences among various studies and plant varieties using a machine-learning approach based on 24 independent bacterial data sets comprising 758 samples and 22 independent fungal data sets comprising 279 samples of healthy or Fusarium wilt-diseased soils from eight different countries. We found that soil bacterial and fungal communities were both clearly separated between diseased and healthy soil samples that originated from six crops across nine countries or regions. Alpha diversity was consistently greater in the fungal community of healthy soils. While diseased soil microbiomes harbored higher abundances of Xanthomonadaceae, Bacillaceae, Gibberella, and Fusarium oxysporum, the healthy soil microbiome contained more Streptomyces Mirabilis, Bradyrhizobiaceae, Comamonadaceae, Mortierella, and nonpathogenic fungi of Fusarium. Furthermore, a random forest method identified 45 bacterial OTUs and 40 fungal OTUs that categorized the health status of the soil with an accuracy >80%. We conclude that these models can be applied to predict the potential for occurrence of F. oxysporum wilt by revealing key biological indicators and features common to the wilt-diseased soil microbiome.

土传植物病害日益给农业生产造成毁灭性损失。开发更精细的疾病预测模型可以通过使用预防性控制措施或种植季节的土壤休耕来帮助减少作物损失。高通量 DNA 测序技术的出现为了解患病土壤与健康土壤的微生物组成提供了前所未有的见解。然而，单个独立案例研究很少得出预测特定土壤中疾病的一般结论。在这里，我们尝试使用机器学习方法来解释各种研究和植物品种之间的差异，该方法基于 24 个独立细菌数据集（包含 758 个样本）和 22 个独立真菌数据集（包含来自 8 个国家的 279 个健康或枯萎病土壤样本）不同的国家。我们发现，来自九个国家或地区的六种作物的患病土壤样本和健康土壤样本之间的土壤细菌和真菌群落都明显分开。健康土壤的真菌群落的阿尔法多样性始终较高。患病土壤微生物群落中黄单胞菌科、芽孢杆菌科、赤霉属和尖镰孢菌的丰度较高，而健康土壤微生物群落中含有更多的紫茉莉链霉菌、缓生根瘤菌科、丛毛单胞菌科、被孢霉和镰刀菌非病原真菌。此外，随机森林方法识别了 45 个细菌 OTU 和 40 个真菌 OTU，对土壤的健康状况进行了分类，准确度 > 80%。 我们的结论是，这些模型可以通过揭示枯萎病土壤微生物组共有的关键生物学指标和特征来预测尖孢镰刀菌枯萎病发生的可能性。