Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt

Agriculture & Biology

• disease occurrence 80%
• Fusarium wilt 76%
• soil 31%
• microbiome 18%
• Otus 16%
• Fusarium oxysporum 13%
• Streptomyces mirabilis 13%
• Fusarium 12%
• Xanthomonadaceae 10%
• Comamonadaceae 10%
• Bacillaceae 9%
• Bradyrhizobiaceae 9%
• Mortierella 9%
• planting season 8%
• disease models 8%
• soil fungi 7%
• crop losses 7%
• artificial intelligence 7%
• high-throughput nucleotide sequencing 7%
• fungal communities 7%
• soil bacteria 6%
• health status 6%
• plant diseases and disorders 6%
• indicator species 6%
• control methods 5%
• soil sampling 5%
• case studies 5%
• sampling 4%
• prediction 4%
• fungi 3%
• crops 3%
• methodology 1%

Medicine & Life Sciences

• Fusarium 82%
• Soil 68%
• Mycobiome 17%
• Microbiota 15%
• Bradyrhizobiaceae 11%
• Xanthomonadaceae 10%
• Gibberella 10%
• Mortierella 9%
• Bacillaceae 9%
• Comamonadaceae 9%
• Plant Diseases 8%
• Datasets 8%
• Mirabilis 7%
• Environmental Biomarkers 7%
• Single-Case Studies 7%
• Streptomyces 6%
• Machine Learning 5%
• High-Throughput Nucleotide Sequencing 5%
• Fungi 4%
• Health Status 4%
• Technology 3%

Earth & Environmental Sciences

• wilt 100%
• soil 26%
• plant disease 7%
• crop 7%
• health status 6%
• control measure 5%
• bioindicator 5%
• loss 5%
• agricultural production 5%
• machine learning 5%
• planting 5%
• fungus 4%
• DNA 4%
• prediction 2%
• method 1%